Verifiable Credential Data Integrity 1.0

Abstract

This section is non-normative.

This specification describes mechanisms for ensuring the authenticity and integrity of Verifiable Credentials and similar types of constrained digital documents using cryptography, especially through the use of digital signatures and related mathematical proofs. Cryptographic proofs enable functionality that is useful to implementors of distributed systems. For example, proofs can be used to:

Make statements that can be shared without loss of trust, because their authorship can be verified by a third party, for example as part of Verifiable Credentials [VC-DATA-MODEL-2.0] or social media posts.
Authenticate as an entity identified by a particular identifier, for example, as the subject identified by a Decentralized Identifier (DID) [DID-CORE].
Delegate authorization for actions in a remote execution environment, via mechanisms such as Authorization Capabilities [ZCAP].
Agree to contracts where the agreement can be verified by another party.
Additionally, many proofs that are based on cryptographic digital signatures provide the benefit of integrity protection, making documents and data tamper-evident.

This section is non-normative.

The operation of Data Integrity is conceptually simple. To create a cryptographic proof, the following steps are performed: 1) Transformation, 2) Hashing, and 3) Proof Generation.

Diagram showing the three steps involved in the creation of a cryptographic
proof. The diagram is laid out left to right with a blue box labeled 'Data'
on the far left. The blue box travels, left to right, through three subsequent
yellow arrows labeled 'Transform Data', 'Hash Data', and 'Generate Proof'. The
resulting blue box at the far right is labeled 'Data with Proof'. — Figure 1 To create a cryptographic proof, data is transformed, hashed, and cryptographically protected.

Transformation is a process described by a transformation algorithm that takes input data and prepares it for the hashing process. One example of a possible transformation is to take a record of people's names that attended a meeting, sort the list alphabetically by the individual's family name, and rewrite the names on a piece of paper, one per line, in sorted order. Examples of transformations include canonicalization and binary-to-text encoding.

Hashing is a process described by a hashing algorithm that calculates an identifier for the transformed data using a cryptographic hash function. This process is conceptually similar to how a phone address book functions, where one takes a person's name (the input data) and maps that name to that individual's phone number (the hash). Examples of cryptographic hash functions include SHA-3 and BLAKE-3.

Proof Generation is a process described by a proof serialization algorithm that calculates a value that protects the integrity of the input data from modification or otherwise proves a certain desired threshold of trust. This process is conceptually similar to the way a wax seal can be used on an envelope containing a letter to establish trust in the sender and show that the letter has not been tampered with in transit. Examples of proof serialization functions include digital signatures and proofs of stake.

To verify a cryptographic proof, the following steps are performed: 1) Transformation, 2) Hashing, and 3) Proof Verification.

Diagram showing the three steps involved in the verification of a cryptographic
proof. The diagram is laid out left to right with a blue box labeled
'Data with Proof' on the far left. The blue box travels, left to right, through
three subsequent yellow arrows labeled 'Transform Data', 'Hash Data', and
'Verify Proof'. The resulting blue box at the far right is labeled 'Data with
Proof'. — Figure 2 To verify a cryptographic proof, data is transformed, hashed, and checked for correctness.

During verification, the transformation and hashing steps are conceptually the same as described above.

Proof Verification is a process that is described by a proof verification algorithm that applies a cryptographic proof verification function to see if the input data can be trusted. Possible proof verification functions include digital signatures and proofs of stake.

This specification details how cryptographic software architects and implementers can package these processes together into things called cryptographic suites and provide them to application developers for the purposes of protecting the integrity of application data in transit and at rest.

This section is non-normative.

This specification optimizes for the following design goals:

Simplicity: The technology is designed to be easy to use for application developers, without requiring significant training in cryptography. It optimizes for the following priority of constituencies: application developers over cryptographic suite implementers, over cryptographic suite designers, over cryptographic algorithm specification authors. The solution focuses on sensible defaults to prevent the selection of ineffective protection mechanisms. See section 5.2 Protecting Application Developers and 5.1 Versioning Cryptography Suites for further details.
Composability: A number of historical digital signature mechanisms have had monolithic designs which limited use cases by combining data transformation, syntax, digital signature, and serialization into a single specification. This specification layers each component such that a broader range of use cases are enabled, including generalized selective disclosure and serialization-agnostic signatures. See section 5.5 Transformations, section 5.7 Data Opacity, and 5.1 Versioning Cryptography Suites for further rationale.
Resilience: Since digital proof mechanisms might be compromised without warning due to technological advancements, it is important that cryptographic suites provide multiple layers of protection and can be rapidly upgraded. This specification provides for both algorithmic agility and cryptographic layering, while still keeping the digital proof format easy for developers to understand and use. See section 5.4 Agility and Layering to understand the particulars.
Progressive Extensibility: Creating and deploying new cryptographic protection mechanisms is designed to be a deliberate, iterative, and careful process that acknowledges that extension happens in phases from experimentation, to implementation, to standardization. This specification strives to balance the need for an increase in the rate of innovation in cryptography with the need for stable production-grade cryptography suites. See section 3. Cryptographic Suites for instructions on establishing new types of cryptographic proofs.
Serialization Flexibility: Cryptographic proofs can be serialized in many different but equivalent ways and have often been tightly bound to the original document syntax. This specification enables one to create cryptographic proofs that are not bound to the original document syntax, which enables more advanced use cases such as being able to use a single digital signature across a variety of serialization syntaxes such as JSON and CBOR without the need to regenerate the cryptographic proof. See section 5.5 Transformations for an explanation of the benefits of such an approach.

Note: Application of technology to broader use cases

While this specification primarily focuses on Verifiable Credentials, the design of this technology is generalized, such that it can be used for non-Verifiable Credential use cases. In these instances, implementers are expected to perform their own due diligence and expert review as to the applicability of the technology to their use case.

As well as sections marked as non-normative, all authoring guidelines, diagrams, examples, and notes in this specification are non-normative. Everything else in this specification is normative.

The key words MAY, MUST, OPTIONAL, and SHOULD in this document are to be interpreted as described in BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all capitals, as shown here.

A conforming secured document is any byte sequence that can be converted to a JSON document that follows the relevant normative requirements in Sections 2.1 Proofs, 2.2 Proof Purposes, 2.3 Resource Integrity, 2.4 Contexts and Vocabularies, and 3.1 DataIntegrityProof.

A conforming cryptographic suite specification is any specification that follows the relevant normative requirements in Section 3. Cryptographic Suites.

A conforming processor is any algorithm realized as software and/or hardware that generates and/or consumes a conforming secured document according to the relevant normative statements in Section 4. Algorithms. Conforming processors MUST produce errors when non-conforming documents are consumed.

This section is non-normative.

This section defines the terms used in this specification. A link to these terms is included whenever they appear in this specification.

controller document

A document that contains public cryptographic material as defined in the Controller Documents 1.0 specification.

cryptographic suite

A specification defining the usage of specific cryptographic primitives in order to achieve a particular security goal. These documents are often used to specify verification methods, digital signature types, their identifiers, and other related properties. See Section 3. Cryptographic Suites for further detail.

data integrity proof

A set of attributes that represent a digital proof and the parameters required to verify it. A digital signature is a type of data integrity proof.

proof purpose

The specific intent for the proof; the reason why an entity created it. The protected declaration acts as a safeguard to prevent the proof from being misused for a purpose other than the one it was intended for.

public key

Cryptographic material that can be used to verify digital proofs created with a corresponding secret key.

secret key

Cryptographic material, sometimes referred to as a private key, that is not to be shared with anyone, and is used to generate digital proofs and/or digital signatures.

verification method

A set of parameters that can be used together with a process to independently verify a proof. For example, a cryptographic public key can be used as a verification method with respect to a digital signature; in such usage, it verifies that the signer possessed the associated cryptographic secret key.

"Verification" and "proof" in this definition are intended to apply broadly. For example, a cryptographic public key might be used during Diffie-Hellman key exchange to negotiate a shared symmetric key for encryption. This guarantees the integrity of the key agreement process. It is thus another type of verification method, even though descriptions of the process might not use the words "verification" or "proof."

verifier

A role an entity performs by receiving data containing one or more data integrity proofs and then determining whether or not the proof is legitimate.

This section specifies the data model that is used for expressing data integrity proofs, controller documents, and verification methods.

All of the data model properties and types in this specification map to URLs. The vocabulary where these URLs are defined is the [SECURITY-VOCABULARY]. The explicit mechanism that is used to perform this mapping in a secured document is the @context property.

The mapping mechanism is defined by JSON-LD [JSON-LD11]. To ensure a document can be interoperably consumed without the use of a JSON-LD library, document authors are advised to ensure that domain experts have 1) specified the expected order for all values associated with a @context property, 2) published cryptographic hashes for each @context file, and 3) deemed that the contents of each @context file are appropriate for the intended use case.

When a document is processed by a non-JSON-LD processor and there is a requirement to use the same semantics as those used in a JSON-LD environment, implementers are advised to 1) enforce the expected order and values in the @context property, and 2) ensure that each @context file matches the known cryptographic hashes for each @context file.

Using static, versioned @context files with published cryptographic hashes in conjunction with JSON Schema is one acceptable approach to implementing the mechanisms described above, which ensures proper term identification, typing, and order, when a non-JSON-LD processor is used.

A data integrity proof provides information about the proof mechanism, parameters required to verify that proof, and the proof value itself. All of this information is provided using Linked Data vocabularies such as the [SECURITY-VOCABULARY].

When expressing a data integrity proof on an object, a proof property MUST be used. The proof property within a Verifiable Credential is a named graph. If present, its value MUST be either a single object, or an unordered set of objects, expressed using the properties below:

id: An optional identifier for the proof, which MUST be a URL [URL], such as a UUID as a URN (urn:uuid:6a1676b8-b51f-11ed-937b-d76685a20ff5). The usage of this property is further explained in Section 2.1.2 Proof Chains.
type: The specific type of proof MUST be specified as a string that maps to a URL [URL]. Examples of proof types include DataIntegrityProof and Ed25519Signature2020. Proof types determine what other fields are required to secure and verify the proof.
proofPurpose: The reason the proof was created MUST be specified as a string that maps to a URL [URL]. The proof purpose acts as a safeguard to prevent the proof from being misused by being applied to a purpose other than the one that was intended. For example, without this value the creator of a proof could be tricked into using cryptographic material typically used to create a Verifiable Credential (assertionMethod) during a login process (authentication) which would then result in the creation of a Verifiable Credential they never meant to create instead of the intended action, which was to merely logging into a website.
verificationMethod: A verification method is the means and information needed to verify the proof. If included, the value MUST be a string that maps to a [URL]. Inclusion of verificationMethod is OPTIONAL, but if it is not included, other properties such as cryptosuite might provide a mechanism by which to obtain the information necessary to verify the proof. Note that when verificationMethod is expressed in a data integrity proof, the value points to the actual location of the data; that is, the verificationMethod references, via a URL, the location of the public key that can be used to verify the proof. This public key data is stored in a controller document, which contains a full description of the verification method.
cryptosuite: An identifier for the cryptographic suite that can be used to verify the proof. See 3. Cryptographic Suites for more information. If the proof type is DataIntegrityProof, cryptosuite MUST be specified; otherwise, cryptosuite MAY be specified. If specified, its value MUST be a string.
created: The date and time the proof was created is OPTIONAL and, if included, MUST be specified as an [XMLSCHEMA11-2] dateTimeStamp string, either in Universal Coordinated Time (UTC), denoted by a Z at the end of the value, or with a time zone offset relative to UTC. Time values that are incorrectly serialized without an offset MUST be interpreted as UTC.
expires: The expires property is OPTIONAL and, if present, specifies when the proof expires. If present, it MUST be an [XMLSCHEMA11-2] dateTimeStamp string, either in Universal Coordinated Time (UTC), denoted by a Z at the end of the value, or with a time zone offset relative to UTC. Time values that are incorrectly serialized without an offset MUST be interpreted as UTC.
domain: The domain property is OPTIONAL. It conveys one or more security domains in which the proof is meant to be used. If specified, the associated value MUST be either a string, or an unordered set of strings. A verifier SHOULD use the value to ensure that the proof was intended to be used in the security domain in which the verifier is operating. The specification of the domain parameter is useful in challenge-response protocols where the verifier is operating from within a security domain known to the creator of the proof. Example domain values include: domain.example (DNS domain), https://domain.example:8443 (Web origin), mycorp-intranet (bespoke text string), and b31d37d4-dd59-47d3-9dd8-c973da43b63a (UUID).
challenge: A string value that SHOULD be included in a proof if a domain is specified. The value is used once for a particular domain and window of time. This value is used to mitigate replay attacks. Examples of a challenge value include: 1235abcd6789, 79d34551-ae81-44ae-823b-6dadbab9ebd4, and ruby.
proofValue: A string value that contains the base-encoded binary data necessary to verify the digital proof using the verificationMethod specified. The contents of the value MUST be expressed with a header and encoding as described in Section 2.4 Multibase of the Controller Documents 1.0 specification. The contents of this value are determined by a specific cryptosuite and set to the proof value generated by the Add Proof Algorithm for that cryptosuite. Alternative properties with different encodings specified by the cryptosuite MAY be used, instead of this property, to encode the data necessary to verify the digital proof.
previousProof: An OPTIONAL string value or unordered list of string values. Each value identifies another data integrity proof that MUST verify before the current proof is processed. If an unordered list, all referenced proofs in the array MUST verify. This property is used in Section 2.1.2 Proof Chains.
nonce: An OPTIONAL string value supplied by the proof creator. One use of this field is to increase privacy by decreasing linkability that is the result of deterministically generated signatures.

A proof can be added to a JSON document like the following:

Example 1: A simple JSON data document

{
  "myWebsite": "https://hello.world.example/"
};

The following proof secures the document above using the eddsa-jcs-2022 cryptography suite [DI-EDDSA], which produces a verifiable digital proof by transforming the input data using the JSON Canonicalization Scheme (JCS) [RFC8785] and then digitally signing it using an Edwards Digital Signature Algorithm (EdDSA).

Example 2: A simple signed JSON data document

{
  "myWebsite": "https://hello.world.example/",
  "proof": {
    "type": "DataIntegrityProof",
    "cryptosuite": "eddsa-jcs-2022",
    "created": "2023-03-05T19:23:24Z",
    "verificationMethod": "https://di.example/issuer#z6MkjLrk3gKS2nnkeWcmcxiZPGskmesDpuwRBorgHxUXfxnG",
    "proofPurpose": "assertionMethod",
    "proofValue": "zQeVbY4oey5q2M3XKaxup3tmzN4DRFTLVqpLMweBrSxMY2xHX5XTYV8nQApmEcqaqA3Q1gVHMrXFkXJeV6doDwLWx"
  }
}

Similarly, a proof can be added to a JSON-LD data document like the following:

Example 3: A simple JSON-LD data document

{
  "@context": {"myWebsite": "https://vocabulary.example/myWebsite"},
  "myWebsite": "https://hello.world.example/"
};

The following proof secures the document above by using the ecdsa-rdfc-2019 cryptography suite [DI-ECDSA], which produces a verifiable digital proof by transforming the input data using the RDF Dataset Canonicalization Scheme [RDF-CANON] and then digitally signing it using the Elliptic Curve Digital Signature Algorithm (ECDSA).

Example 4: A simple signed JSON-LD data document

{
  "@context": [
    {"myWebsite": "https://vocabulary.example/myWebsite"},
    "https://w3id.org/security/data-integrity/v2"
  ],
  "myWebsite": "https://hello.world.example/",
  "proof": {
    "type": "DataIntegrityProof",
    "cryptosuite": "ecdsa-rdfc-2019",
    "created": "2020-06-11T19:14:04Z",
    "verificationMethod": "https://ldi.example/issuer#zDnaepBuvsQ8cpsWrVKw8fbpGpvPeNSjVPTWoq6cRqaYzBKVP",
    "proofPurpose": "assertionMethod",
    "proofValue": "zXb23ZkdakfJNUhiTEdwyE598X7RLrkjnXEADLQZ7vZyUGXX8cyJZRBkNw813SGsJHWrcpo4Y8hRJ7adYn35Eetq"
  }
}

Note: Representing time values to individuals

This specification enables the expression of dates and times, such as through the created and expires properties. This information might be indirectly exposed to an individual if a proof is processed and is detected to be outside an allowable time range. When displaying date and time values related to the validity of cryptographic proofs, implementers are advised to respect the locale and local calendar preferences of the individual [LTLI]. Conversion of timestamps to local time values are expected to consider the time zone expectations of the individual. See Verifiable Credentials Data Model v2.0 for more details about representing time values to individuals.

Issue 1

Add a note indicating that selective disclosure proof mechanisms can be compatible with Data Integrity; for example, an algorithm could produce a merkle tree from a canonicalized set of N-Quads and then sign the root hash. Disclosure would involve including the merkle paths for each N-Quad that is to be revealed. This mechanism would merely consume the normalized output differently (this, and the proof mechanism would be modifications to this core spec). It might also be necessary to generate proof parameters such as a secret key/seed that can be used along with an algorithm to deterministically generate nonces that are concatenated with each N-Quad to prevent rainbow table or similar attacks.

Issue 2

Add a note indicating that this specification should not be construed to indicate that public key controllers should be restricted to a single public key or that systems that use this spec and involve real people should identify each person as only ever being a single entity rather than perhaps N entities with M keys. There are no such restrictions and in many cases those kinds of restrictions are ill-advised due to privacy considerations.

The Data Integrity specification supports the concept of multiple proofs in a single document. There are two types of multi-proof approaches that are identified: Proof Sets (un-ordered) and Proof Chains (ordered).

A proof set is useful when the same data needs to be secured by multiple entities, but where the order of proofs does not matter, such as in the case of a set of signatures on a contract. A proof set, which has no order, is represented by associating a set of proofs with the proof key in a document.

Example 5: A proof set in a data document

{
  "@context": [
    {"myWebsite": "https://vocabulary.example/myWebsite"},
    "https://w3id.org/security/data-integrity/v2"
],
  "myWebsite": "https://hello.world.example/",
  "proof": [{
    "type": "DataIntegrityProof",
    "cryptosuite": "eddsa-rdfc-2022",
    "created": "2020-11-05T19:23:24Z",
    "verificationMethod": "https://ldi.example/issuer/1#z6MkjLrk3gKS2nnkeWcmcxiZPGskmesDpuwRBorgHxUXfxnG",
    "proofPurpose": "assertionMethod",
    "proofValue": "z4oey5q2M3XKaxup3tmzN4DRFTLVqpLMweBrSxMY2xHX5XTYVQeVbY8nQAVHMrXFkXJpmEcqdoDwLWxaqA3Q1geV6"
  }, {
    "type": "DataIntegrityProof",
    "cryptosuite": "eddsa-rdfc-2022",
    "created": "2020-11-05T13:08:49Z",
    "verificationMethod": "https://pfps.example/issuer/2#z6MkGskxnGjLrk3gKS2mesDpuwRBokeWcmrgHxUXfnncxiZP",
    "proofPurpose": "assertionMethod",
    "proofValue": "z5QLBrp19KiWXerb8ByPnAZ9wujVFN8PDsxxXeMoyvDqhZ6Qnzr5CG9876zNht8BpStWi8H2Mi7XCY3inbLrZrm95"
  }]
}

A proof chain is useful when the same data needs to be signed by multiple entities and the order of when the proofs occurred matters, such as in the case of a notary counter-signing a proof that had been created on a document. A proof chain, where proof order needs to be preserved, is expressed by providing at least one proof with an id, such as a UUID as a URN, and another proof with a previousProof value that identifies the previous proof.

Example 6: A proof chain in a data document

{
  "@context": [
    {"myWebsite": "https://vocabulary.example/myWebsite"},
    "https://w3id.org/security/data-integrity/v2"
],
  "myWebsite": "https://hello.world.example/",
  "proof": [{
    "id": "urn:uuid:60102d04-b51e-11ed-acfe-2fcd717666a7",
    "type": "DataIntegrityProof",
    "cryptosuite": "eddsa-rdfc-2022",
    "created": "2020-11-05T19:23:42Z",
    "verificationMethod": "https://ldi.example/issuer/1#z6MkjLrk3gKS2nnkeWcmcxiZPGskmesDpuwRBorgHxUXfxnG",
    "proofPurpose": "assertionMethod",
    "proofValue": "zVbY8nQAVHMrXFkXJpmEcqdoDwLWxaqA3Q1geV64oey5q2M3XKaxup3tmzN4DRFTLVqpLMweBrSxMY2xHX5XTYVQe"
  }, {
    "type": "DataIntegrityProof",
    "cryptosuite": "eddsa-rdfc-2022",
    "created": "2020-11-05T21:28:14Z",
    "verificationMethod": "https://pfps.example/issuer/2#z6MkGskxnGjLrk3gKS2mesDpuwRBokeWcmrgHxUXfnncxiZP",
    "proofPurpose": "assertionMethod",
    "proofValue": "z6Qnzr5CG9876zNht8BpStWi8H2Mi7XCY3inbLrZrm955QLBrp19KiWXerb8ByPnAZ9wujVFN8PDsxxXeMoyvDqhZ",
    "previousProof": "urn:uuid:60102d04-b51e-11ed-acfe-2fcd717666a7"
  }]
}

When securing data in a document, it is important to clearly delineate the data being protected, which is every graph expressed in the document except the one containing the data associated with a securing mechanism, which is called a proof graph. Creating this separation enables the processing algorithms to deterministically protect and verify a secured document.

The information contained in an input document before a data integrity proof is added to the document is expressed in one or more graphs. To ensure that information from different data integrity proofs is not accidentally co-mingled, the concept of a proof graph is used to encapsulate each data integrity proof. Each value associated with the proof property of the document identifies a separate graph, which is sometimes referred to as a named graph, of type ProofGraph, which contains a single data integrity proof.

Using these graphs has a concrete effect when performing JSON-LD processing, as this properly separates statements expressed in one graph from those in another graph. Implementers that limit their processing to other media types, such as JSON, YAML, or CBOR, will need to keep this in mind if they merge data from one document with data from another, such as when an id value string is the same in both documents. It is important to not merge objects that seem to have similar properties, when those objects do not have an id property and/or use a global identifier type such as a URL, as without these, is not possible to tell whether two such objects are expressing information about the same entity.

A proof that describes its purpose helps prevent it from being misused for some other purpose.

Issue 3

Add a mention of JWK's key_ops parameter and WebCrypto's KeyUsage restrictions; explain that Proof Purpose serves a different goal and allows for finer-grained restrictions.

Dave Longley suggested that proof purposes enable verifiers to know what the proof creator's intent was so the message can't be accidentally abused for another purpose, e.g., a message signed for the purpose of merely making an assertion (and thus perhaps intended to be widely shared) being abused as a message to authenticate to a service or take some action (invoke a capability). It's a goal to keep the number of them limited to as few categories as are really needed to accomplish this goal.

The following is a list of commonly used proof purpose values.

authentication: Indicates that a given proof is only to be used for the purposes of an authentication protocol.
assertionMethod: Indicates that a proof can only be used for making assertions, for example signing a Verifiable Credential.
keyAgreement: Indicates that a proof is used for for key agreement protocols, such as Elliptic Curve Diffie Hellman key agreement used by popular encryption libraries.
capabilityDelegation: Indicates that the proof can only be used for delegating capabilities. See the Authorization Capabilities [ZCAP] specification for more detail.
capabilityInvocation: Indicates that the proof can only be used for invoking capabilities. See the Authorization Capabilities [ZCAP] specification for more detail.

Note: The Authorization Capabilities [ZCAP] specification defines additional proof purposes for that use case, such as capabilityInvocation and capabilityDelegation.

When a link to an external resource is included in a conforming secured document, it is desirable to know whether the resource that is identified has changed since the proof was created. This applies to cases where there is an external resource that is remotely retrieved as well as to cases where the verifier might have a locally cached copy of the resource.

To enable confirmation that a resource referenced by a conforming secured document has not changed since the document was secured, an implementer MAY include a property named digestMultibase in any object that includes an id property. If present, the digestMultibase value MUST be a single string value, or an array of string values, each of which is a Multibase-encoded Multihash value.

JSON-LD context authors are expected to add digestMultibase to contexts that will be used in documents that refer to other resources and to include an associated cryptographic digest. For example, the Verifiable Credentials Data Model v2.0 refers to context (https://www.w3.org/ns/credentials/v2) which includes digestMultibase, and the Verifiable Credentials Data Model v2.0 includes the hexadecimal encoded SHA2-256 digest value of that context document.

An example of a resource integrity protected object is shown below:

Example 7: An integrity-protected image that is associated with an object

{
  ...
  "image": {
    "id": "https://university.example.org/images/58473",
    "digestMultibase": "zQmdfTbBqBPQ7VNxZEYEj14VmRuZBkqFbiwReogJgS1zR1n"
  },
  ...
}

Implementers are urged to consult appropriate sources, such as the FIPS 180-4 Secure Hash Standard and the Commercial National Security Algorithm Suite 2.0 to ensure that they are choosing a hash algorithm that is appropriate for their use case.

Issue 4: (AT RISK) Hash values might change during Candidate Recommendation

This section lists cryptographic hash values that might change during the Candidate Recommendation phase based on implementer feedback that requires the referenced files to be modified.

Implementations that perform JSON-LD processing MUST treat the following JSON-LD context URLs as already resolved, where the resolved document matches the corresponding hash values below:

Context URL and Hash
URL: https://w3id.org/security/data-integrity/v2 SHA2-256 Digest: `67f21e6e33a6c14e5ccfd2fc7865f7474fb71a04af7e94136cb399dfac8ae8f4`
URL: https://w3id.org/security/multikey/v1 SHA2-256 Digest: `ba2c182de2d92f7e47184bcca8fcf0beaee6d3986c527bf664c195bbc7c58597`
URL: https://w3id.org/security/jwk/v1 SHA2-256 Digest: `0f14b62f6071aafe00df265770ea0c7508e118247d79b7d861a406d2aa00bece`

It is possible to confirm the cryptographic digests listed above by running a command like the following (replacing <DOCUMENT_URL> with the appropriate value) through a modern UNIX-like OS command line interface: curl -sL -H "Accept: application/ld+json" <DOCUMENT_URL> | openssl dgst -sha256

The security vocabulary terms that the JSON-LD contexts listed above resolve to are in the https://w3id.org/security# namespace. That is, all security terms in this vocabulary are of the form https://w3id.org/security#TERM, where TERM is the name of a term.

Implementations that perform RDF processing MUST treat the JSON-LD serialization of the vocabulary URL as already dereferenced, where the dereferenced document matches the corresponding hash value below.

When dereferencing the https://w3id.org/security# URL, the media type of the data that is returned depends on HTTP content negotiation. These are as follows:

Media Type	Description and Hash
application/ld+json	The vocabulary in JSON-LD format [JSON-LD11]. SHA2-256 Digest: `15585792bfaaeb2b6dcde62d75298b554025a7bfdad0a0832530195eda95f04f`
text/turtle	The vocabulary in Turtle format [TURTLE]. SHA2-256 Digest: `afeb30493e7a677b6f5e284f9f3fbfeb09b8bc88998ec17ff8964b4027c5d655`
text/html	The vocabulary in HTML+RDFa Format [HTML-RDFA]. SHA2-256 Digest: `b0193d3f60176d1b6fa7f1d115e1d0e4d870ece2b24fc584f1e99d2dc0582562`

It is possible to confirm the cryptographic digests listed above by running a command like the following (replacing <MEDIA_TYPE> and <DOCUMENT_URL> with the appropriate values) through a modern UNIX-like OS command line interface: curl -sL -H "Accept: <MEDIA_TYPE>" <DOCUMENT_URL> | openssl dgst -sha256

Authors of application-specific vocabularies and specifications SHOULD ensure that their JSON-LD context and vocabulary files are permanently cacheable using the approaches to caching described above or a functionally equivalent mechanism.

Implementations MAY load application-specific JSON-LD context files from the network during development, but SHOULD permanently cache JSON-LD context files used by conforming secured documents in production settings, to increase their security and privacy characteristics. Goals of processing speed MAY be achieved through caching approaches such as those described above or functionally equivalent mechanisms.

Some applications, such as digital wallets, that are capable of holding arbitrary verifiable credentials or other data-integrity-protected documents, from any issuer and using any contexts, might need to be able to load externally linked resources, such as JSON-LD context files, in production settings. This is expected to increase user choice, scalability, and decentralized upgrades in the ecosystem over time. Authors of such applications are advised to read the security and privacy sections of this document for further considerations.

For further information regarding processing of JSON-LD contexts and vocabularies, see Verifiable Credentials v2.0: Base Context and Verifiable Credentials v2.0: Vocabularies.

It is necessary to ensure that a consuming application has explicitly approved of the types, and therefore the semantics, of input documents that it will process. Not checking JSON-LD context values against known good values can lead to security vulnerabilities, due to variance in the semantics that they convey. Applications MUST use the algorithm in Section 4.6 Context Validation, or one that achieves equivalent protections, to validate contexts in a conforming secured document. Context validation MUST be run after running the applicable algorithm in either Section 4.4 Verify Proof or Section 4.5 Verify Proof Sets and Chains.

While the algorithm described in Section 4.6 Context Validation provides one way of checking context values, and one optional way of safely processing unknown context values, implementers MAY use alternative approaches, or a different ordering of the steps, that provide the same protections.

For example, if no JSON-LD processing is to occur, then, rather than performing this check, an application could follow the guidance in whatever trusted documentation is provided out of band for properly understanding the semantics of that type of document.

Another approach would be to configure an application to use a JSON-LD Context loader, sometimes referred to as a document loader, to use only local copies of approved context files. This would guarantee that neither the context files nor their cryptographic hashes would ever change, effectively resulting in the same result as the algorithm in Section 4.6 Context Validation.

Another alternative approach, also effectively equivalent to the algorithm in Section 4.6 Context Validation, would be for an application to keep a list of well known context URLs and their associated approved cryptographic hashes, without storing every context file locally. This would allow these contexts to be safely loaded from the network without compromising the security expectations of the application.

Yet another valid approach would be for a transmitting application to compact a document to exactly what a receiving application requests, via a protocol such as one requesting a verifiable presentation, omitting additional sender-specific context values that were used when securing the original document. As long as the cryptography suite's verification algorithm provides a successful verification result, such transformations are valid and would result in full URLs for terms that were previously compacted by the omitted context. That is, a term that was previously compacted to foo based on a sender-supplied context that is unknown to a receiver (e.g., `https://ontology.example/v1) would instead be "expanded" to a URL like https://ontology.example#foo, which would then be "compacted" to the same URL, once the unknown context is omitted and the JSON-LD compaction algorithm is applied by the receiving application.

The @context property is used to ensure that implementations are using the same semantics when terms in this specification are processed. For example, this can be important when properties like type are processed and its value, such as DataIntegrityProof, are used.

When securing a document, if an @context property is not provided in the document or the Data Integrity terms used in the document are not mapped by existing values in the @context property, implementations MUST inject or add an @context property with a value of https://w3id.org/security/data-integrity/v2.

Context injection is expected to be unnecessary sometimes, such as when the Verifiable Credential Data Model v2.0 context (https://www.w3.org/ns/credentials/v2) exists as a value in the @context property, as that context maps all of the necessary Data Integrity terms that were previously mapped by https://w3id.org/security/data-integrity/v2.

HTML processors are designed to continue processing if recoverable errors are detected. JSON-LD processors operate in a similar manner. This design philosophy was meant to ensure that developers could use only the parts of the JSON-LD language that they find useful, without causing the processor to throw errors on things that might not be important to the developer. Among other effects, this philosophy led to JSON-LD processors being designed to not throw errors, but rather warn developers, when encountering things such as undefined terms.

When converting from JSON-LD to an RDF Dataset, such as when canonicalizing a document [RDF-CANON], undefined terms and relative URLs can be dropped silently. When values are dropped, they are not protected by a digital proof. This creates a mismatch of expectations, where a developer, who is unaware of how a JSON-LD processor works, might think that certain data was being secured, and then be surprised to find that it was not, when no error was thrown. This specification requires that any recoverable loss of data when performing JSON-LD transformations result in an error, to avoid a mismatch in the security expectations of developers.

Implementations that use JSON-LD processing, such as RDF Dataset Canonicalization [RDF-CANON], MUST throw an error, which SHOULD be DATA_LOSS_DETECTION_ERROR, when data is dropped by a JSON-LD processor, such as when an undefined term is detected in an input document.

Similarly, since conforming secured documents can be transferred from one security domain to another, conforming processors that process the conforming secured document cannot assume any particular base URL for the document. When deserializing to RDF, implementations MUST ensure that the base URL is set to null.

This section defines datatypes that are used by this specification.

This specification encodes cryptographic suite identifiers as enumerable strings, which is useful in processes that need to efficiently encode such strings, such as compression algorithms. In environments that support data types for string values, such as RDF [RDF-CONCEPTS], cryptographic identifier content is indicated using a literal value whose datatype is set to https://w3id.org/security#cryptosuiteString.

The cryptosuiteString datatype is defined as follows:

The URL denoting this datatype: https://w3id.org/security#cryptosuiteString
The lexical space: The union of all cryptosuite strings, expressed using American Standard Code for Information Interchange [ASCII] strings, that are defined by the collection of all Data Integrity cryptosuite specifications.
The value space: The union of all cryptosuite types that are expressed using the cryptosuite property, as defined in Section 3.1 DataIntegrityProof.
The lexical-to-value mapping: Any element of the lexical space is mapped to the result of parsing it into an internal representation that uniquely identifies the cryptosuite type from all other possible cryptosuite types.
The canonical mapping: Any element of the value space is mapped to the corresponding string in the lexical space.

Multibase-encoded strings are used to encode binary data into ASCII-only formats, which are useful in environments that cannot directly represent binary values. This specification makes use of this encoding. In environments that support data types for string values, such as RDF [RDF-CONCEPTS], Multibase-encoded content is indicated using a literal value whose datatype is set to https://w3id.org/security#multibase.

The multibase datatype is defined as follows:

The URL denoting this datatype: https://w3id.org/security#multibase
The lexical space: Any string that starts with a Multibase character and the rest of the characters consist of allowable characters in the respective base-encoding alphabet.
The value space: The standard mathematical concept of all integer numbers.
The lexical-to-value mapping: Any element of the lexical space is mapped to the value space by base-decoding the value based on the base-decoding alphabet associated with the first Multibase character in the lexical string.
The canonical mapping: The canonical mapping consists of using the lexical-to-value mapping.

The term Linked Data is used to describe a recommended best practice for exposing, sharing, and connecting information on the Web using standards, such as URLs, to identify things and their properties. When information is presented as Linked Data, other related information can be easily discovered and new information can be easily linked to it. Linked Data is extensible in a decentralized way, greatly reducing barriers to large scale integration.

With the increase in usage of Linked Data for a variety of applications, there is a need to be able to verify the authenticity and integrity of Linked Data documents. This specification adds authentication and integrity protection to data documents through the use of mathematical proofs without sacrificing Linked Data features such as extensibility and composability.

Note: Use of Linked Data is an optional feature

While this specification provides mechanisms to digitally sign Linked Data, the use of Linked Data is not necessary to gain some of the advantages provided by this specification.

Cryptographic suites that implement this specification can be used to secure verifiable credentials and verifiable presentations. Implementers that are addressing those use cases are cautioned that additional checks might be appropriate when processing those types of documents.

There are some use cases where it is important to ensure that the verification method used in a proof is associated with the issuer in a verifiable credential, or the holder in a verifiable presentation, during the process of validation. One way to check for such an association is to ensure that the value of the controller property of a proof's verification method matches the URL value used to identify the issuer or holder, respectively, and that the verification method is expressed under a verification relationship that is acceptable given the proof's purpose. This particular association indicates that the issuer or holder, respectively, is the controller of the verification method used to verify the proof.

Document authors and implementers are advised to understand the difference between the validity period of a proof, which is expressed using the created and expires properties, and the validity period of a credential, which is expressed using the validFrom and validUntil properties. While these properties might sometimes express the same validity periods, at other times they might not be aligned. When verifying a proof, it is important to ensure that the time of interest (which might be the current time or any other time) is within the validity period for the proof (that is, between created and expires ). When validating a verifiable credential, it is important to ensure that the time of interest is within the validity period for the credential (that is, betweeen validFrom and validUntil). Note that a failure to validate either the validity period for the proof, or the validity period for the credential, might result in accepting data that ought to have been rejected.

Finally, implementers are also urged to understand that there is a difference between the revocation information associated with a verifiable credential, and the revocation and expiration times for a verification method. The revocation and expiration times for a verification method are expressed using the revocation and expires properties, respectively; are related to events such as a secret key being compromised or expiring; and can provide timing information which might reveal details about a controller, such as their security practices or when they might have been compromised. The revocation information for a verifiable credential is expressed using the credentialStatus property; is related to events such as an individual losing the privilege that is granted by the verifiable credential; and does not provide timing information, which enhances privacy.

The algorithms defined below operate on documents represented as JSON objects. This specification follows the JSON-LD 1.1 Processing Algorithms and API specification in representing a JSON object as an map. An unsecured data document is a map that contains no proof values. An input document is an map that has not yet had the current proof added to it, but it MAY contain a proof value that was added to it by a previous process. A secured data document is a map that contains one or more proof value, one of which might be the current proof(s) being generated to be added to it.

Implementers MAY implement reasonable defaults and safeguards in addition to the algorithms below, to help mitigate developer error, excessive resource consumption, newly discovered attack models against which there is a particular protection, etc. The algorithms provided below are the minimum requirements for an interoperable implementation, and developers are urged to include additional measures that could contribute to a safer and more efficient ecosystem.

The processing model used by a conforming processor and its application-specific software is described in this section. When software is to ensure information is tamper-evident, it performs the following steps:

The software arranges the information into a document, such as a JSON or JSON-LD document.
If the document is a JSON-LD document, the software selects one or more JSON-LD Contexts and expresses them using the @context property.
The software selects one or more cryptography suites that meet the needs of the use case, such as one that provides full, selective, or unlinkable disclosure, using acceptable cryptographic key material.
The software uses the applicable algorithms in either Section 4.2 Add Proof or Section 4.3 Add Proof Set/Chain to add one or more proofs.

When software needs to use information that was transmitted to it using a mechanism described by this specification, it performs the following steps:

The software transforms the incoming data into a document that can be understood by the applicable algorithm provided in Section 4.4 Verify Proof or Section 4.5 Verify Proof Sets and Chains.
The software uses JSON Schema or equivalent mechanism to validate that the incoming document follows an expected schema used by the application.
The software uses the applicable algorithms provided in Section 4.4 Verify Proof or Section 4.5 Verify Proof Sets and Chains to verify the integrity of the incoming document.
If the document is a JSON-LD document, the software uses the algorithm provided in Section 4.6 Context Validation, or one providing equivalent protections, to validate all JSON-LD Context values used in the document.

The following algorithm specifies how a digital proof can be added to an input document, and can then be used to verify the output document's authenticity and integrity. Required inputs are an input document (map inputDocument), a cryptosuite instance (struct cryptosuite), and a set of options (map options). Output is a secured data document (map) or an error. Whenever this algorithm encodes strings, it MUST use UTF-8 encoding.

Let proof be the result of calling the createProof algorithm specified in cryptosuite.createProof with inputDocument and options passed as a parameters. If the algorithm produces an error, the error MUST be propagated and SHOULD convey the error type.
If one or more of the proof.type, proof.verificationMethod, and proof.proofPurpose values is not set, an error MUST be raised and SHOULD convey an error type of PROOF_GENERATION_ERROR.
If options has a non-null domain item, it MUST be equal to proof.domain or an error MUST be raised and SHOULD convey an error type of PROOF_GENERATION_ERROR.
If options has a non-null challenge item, it MUST be equal to proof.challenge or an error MUST be raised and SHOULD convey an error type of PROOF_GENERATION_ERROR.
Let securedDataDocument be a copy of inputDocument.
Set securedDataDocument.proof to the value of proof.
Return securedDataDocument as the secured data document.

The following algorithm specifies how to incrementally add a proof to a proof set or proof chain starting with a secured document containing either a proof or proof set/chain. Required inputs are a secured data document (map securedDocument), a cryptographic suite (suite), and a set of options (map options). Output is a new secured data document (map). Whenever this algorithm encodes strings, it MUST use UTF-8 encoding.

Let proof be set to securedDocument.proof. Let allProofs be an empty list. If proof is a list, copy all the elements of proof to allProofs. If proof is an object add a copy of that object to allProofs.
Let the inputDocument be a copy of the securedDocument with the proof attribute removed. Let output be a copy of the inputDocument.
Let matchingProofs be an empty list.
If options has a previousProof item that is a string, add the element from allProofs with an id attribute matching previousProof to matchingProofs. If a proof with id equal to previousProof does not exist in allProofs, an error MUST be raised and SHOULD convey an error type of PROOF_GENERATION_ERROR.
If options has a previousProof item that is an array, add each element from allProofs with an id attribute that matches an element of that array. If any element of previousProof array has an id attribute that does not match the id attribute of any element of allProofs, an error MUST be raised and SHOULD convey an error type of PROOF_GENERATION_ERROR.
Set inputDocument.proof to matchingProofs.
Note
This step is critical, as it binds the previous proofs to the document prior to signing. The proof value for the document will be updated in a later step of this algorithm.
Run steps 1 through 6 of the algorithm in section 4.2 Add Proof, passing inputDocument, suite, and options. If no exceptions are raised, append the generated proof value to the allProofs; otherwise, raise the exception.
Set output.proof to the value of allProofs.
Return output as the new secured data document.

The following algorithm specifies how to check the authenticity and integrity of a secured data document by verifying its digital proof. The algorithm takes as input:

mediaType: A media type
documentBytes: A byte sequence whose media type is mediaType
cryptosuite: A cryptosuite instance
expectedProofPurpose: An optional string, used to ensure that the proof was generated by the proof creator for the expected reason by the verifier. See 2.2 Proof Purposes for common values
domain: An optional set of strings, used by the proof creator to lock a proof to a particular security domain, and used by the verifier to ensure that a proof is not being used across different security domains
challenge: An optional string challenge, used by the verifier to ensure that an attacker is not replaying previously created proofs

This algorithm returns a verification result, a struct whose items are:

verified: true or false
verifiedDocument: Null, if verified is false; otherwise, an input document
media type: Null, if verified is false; otherwise, a media type, which MAY include parameters
warnings: a list of ProblemDetails, which defaults to an empty list
errors: a list of ProblemDetails, which defaults to an empty list

When a step says "an error MUST be raised", it means that a verification result MUST be returned with a verified of false and a non-empty errors list.

Let securedDocument be the result of running parse JSON bytes to an Infra value on documentBytes.
If either securedDocument is not a map or securedDocument.proof is not a map, an error MUST be raised and SHOULD convey an error type of PARSING_ERROR.
Let proof be securedDocument.proof.
If one or more of proof.type, proof.verificationMethod, and proof.proofPurpose does not exist, an error MUST be raised and SHOULD convey an error type of PROOF_VERIFICATION_ERROR.
If expectedProofPurpose was given, and it does not match proof.proofPurpose, an error MUST be raised and SHOULD convey an error type of PROOF_VERIFICATION_ERROR.
If domain was given, and it does not contain the same strings as proof.domain (treating a single string as a set containing just that string), an error MUST be raised and SHOULD convey an error type of INVALID_DOMAIN_ERROR.
If challenge was given, and it does not match proof.challenge, an error MUST be raised and SHOULD convey an error type of INVALID_CHALLENGE_ERROR.
Let cryptosuiteVerificationResult be the result of running the cryptosuite.verifyProof algorithm with securedDocument provided as input.
Return a verification result with items:

verified

cryptosuiteVerificationResult.verified

verifiedDocument

cryptosuiteVerificationResult.verifiedDocument

media type

mediaType

In a proof set or proof chain, a secured data document has a proof attribute which contains a list of proofs (allProofs). The following algorithm provides one method of checking the authenticity and integrity of a secured data document, achieved by verifying every proof in allProofs. Other approaches are possible, particularly if it is only desired to verify a subset of the proofs contained in allProofs. If another approach is taken to verify only a subset of the proofs, then it is important to note that any proof in that subset with a previousProof can only be considered verified if the proofs it references are also considered verified.

Required input is a secured data document (securedDocument). A list of verification results corresponding to each proof in allProofs is generated, and a single combined verification result is returned as output. Implementations MAY return any of the other verification results and/or any other metadata alongside the combined verification result.

Set allProofs to securedDocument.proof.
Set verificationResults to an empty list.
For each proof in allProofs, do the following steps:
1. Let matchingProofs be an empty list.
2. If proof contains a previousProof attribute and that attribute is a string, add the element from allProofs with an id attribute matching previousProof to matchingProofs. If a proof with id does not exist in allProofs, an error MUST be raised and SHOULD convey an error type of PROOF_VERIFICATION_ERROR. If the previousProof attribute is an array, add each element from allProofs with an id attribute that matches an element of that array. If any element of previousProof array has an id attribute that does not match the id attribute of any element of allProofs, an error MUST be raised and SHOULD convey an error type of PROOF_VERIFICATION_ERROR.
3. Let inputDocument be a copy of securedDocument with the proof value removed and then set inputDocument.proof to matchingProofs.
4. Run steps 4 through 8 of the algorithm in section 4.4 Verify Proof on inputDocument; if no exceptions are raised, append cryptosuiteVerificationResult to verificationResults.
Set successfulVerificationResults to an empty list.
Let combinedVerificationResult be an empty struct. Set combinedVerificationResult.status to true, combinedVerificationResult.document to null, and combinedVerificationResult.mediaType to null.
For each cryptosuiteVerificationResult in verificationResults:
1. If cryptosuiteVerificationResult.verified is false, set combinedVerificationResult.verified to false.
2. Otherwise, set combinedVerificationResult.document to cryptosuiteVerificationResult.verifiedDocument, set combinedVerificationResult.mediaType to cryptosuiteVerificationResult.mediaType, and append cryptosuiteVerificationResult to successfulVerificationResults.
If combinedVerificationResult.status is false, set combinedVerificationResult.document to null and combinedVerificationResult.mediaType to null.
Return combinedVerificationResult, successfulVerificationResults.

The following algorithm provides one mechanism that can be used to ensure that an application understands the contexts associated with a document before it executed business rules specific to the input in the document. For more rationale related to this algorithm, see Section 2.4.1 Validating Contexts. This algorithm takes inputs of a document (map inputDocument), a set of approved JSON-LD Contexts (map expectedContext), and a boolean to recompact when unknown contexts are detected (boolean recompact), and returns a map that contains the following:

a status (boolean status)
a validated document (map document) if no errors occurred
zero or more warnings (list of ProblemDetails warnings)
zero or more errors (list of ProblemDetails errors)

The context validation algorithm is as follows:

Set result.status to false, result.warnings to an empty list, result.errors to an empty list, compactionContext to an empty list; and clone inputDocument to result.document.
Let contextValue be the value of the @context property of result.document, which might be undefined.
If contextValue does not deeply equal expectedContext, then if any subtree in result.document contains an @context property or any URI in contextValue dereferences to a JSON-LD Context file that does not match a known good value or cryptographic hash, then perform the applicable action:
1. If recompact is true, set result.document to the result of running the JSON-LD Compaction Algorithm with the inputDocument and expectedContext as inputs. If the compaction fails, add at least one error to result.errors.
2. If recompact is not true, add at least one error to result.errors.
If result.errors is empty, set result.status to true; otherwise, set result.status to false, and remove the document property from result.
Return the value of result.

Implementations MAY include additional warnings or errors that enforce further validation rules that are specific to the implementation or a particular use case.

The algorithms described in this specification, as well as in various cryptographic suite specifications, throw specific types of errors. Implementers might find it useful to convey these errors to other libraries or software systems. This section provides specific URLs, descriptions, and error codes for the errors, such that an ecosystem implementing technologies described by this specification might interoperate more effectively when errors occur.

When exposing these errors through an HTTP interface, implementers SHOULD use [RFC9457] to encode the error data structure. If [RFC9457] is used:

The type value of the error object MUST be a URL that starts with the value https://w3id.org/security# and ends with the value in the section listed below.
The code value MUST be the integer code described in the table below (in parentheses, beside the type name).
The title value SHOULD provide a short but specific human-readable string for the error.
The detail value SHOULD provide a longer human-readable string for the error.

PROOF_GENERATION_ERROR (-16): A request to generate a proof failed. See Section 4.2 Add Proof, and Section 4.3 Add Proof Set/Chain.
PROOF_VERIFICATION_ERROR (-17): An error was encountered during proof verification. See Section 4.4 Verify Proof.
PROOF_TRANSFORMATION_ERROR (-18): An error was encountered during the transformation process.
INVALID_DOMAIN_ERROR (-19): The domain value in a proof did not match the expected value. See Section 4.4 Verify Proof.
INVALID_CHALLENGE_ERROR (-20): The challenge value in a proof did not match the expected value. See Section 4.4 Verify Proof.

The following section describes security considerations that developers implementing this specification should be aware of in order to create secure software.

Cryptography secures information through the use of secrets. Knowledge of the necessary secret makes it computationally easy to access certain information. The same information can be accessed if a computationally-difficult, brute-force effort successfully guesses the secret. All modern cryptography requires the computationally difficult approach to remain difficult throughout time, which does not always hold due to breakthroughs in science and mathematics. That is to say that Cryptography has a shelf life.

This specification plans for the obsolescence of all cryptographic approaches by asserting that whatever cryptography is in use today is highly likely to be broken over time. Software systems have to be able to change the cryptography in use over time in order to continue to secure information. Such changes might involve increasing required secret sizes or modifications to the cryptographic primitives used. However, some combinations of cryptographic parameters might actually reduce security. Given these assumptions, systems need to be able to distinguish different combinations of safe cryptographic parameters, also known as cryptographic suites, from one another. When identifying or versioning cryptographic suites, there are several approaches that can be taken which include: parameters, numbers, and dates.

Parametric versioning specifies the particular cryptographic parameters that are employed in a cryptographic suite. For example, one could use an identifier such as RSASSA-PKCS1-v1_5-SHA1. The benefit to this scheme is that a well-trained cryptographer will be able to determine all of the parameters in play by the identifier. The drawback to this scheme is that most of the population that uses these sorts of identifiers are not well trained and thus will not understand that the previously mentioned identifier is a cryptographic suite that is no longer safe to use. Additionally, this lack of knowledge might lead software developers to generalize the parsing of cryptographic suite identifiers such that any combination of cryptographic primitives becomes acceptable, resulting in reduced security. Ideally, cryptographic suites are implemented in software as specific, acceptable profiles of cryptographic parameters instead.

Numbered versioning might specify a major and minor version number such as 1.0 or 2.1. Numbered versioning conveys a specific order and suggests that higher version numbers are more capable than lower version numbers. The benefit of this approach is that it removes complex parameters that less expert developers might not understand with a simpler model that conveys that an upgrade might be appropriate. The drawback of this approach is that its not clear if an upgrade is necessary, as software version number increases often don't require an upgrade for the software to continue functioning. This can lead to developers thinking their usage of a particular version is safe, when it is not. Ideally, additional signals would be given to developers that use cryptographic suites in their software that periodic reviews of those suites for continued security are required.

Date-based versioning specifies a particular release date for a specific cryptographic suite. The benefit of a date, such as a year, is that it is immediately clear to a developer if the date is relatively old or new. Seeing an old date might prompt the developer to go searching for a newer cryptographic suite, where as a parametric or number-based versioning scheme might not. The downside of a date-based version is that some cryptographic suites might not expire for 5-10 years, prompting the developer to go searching for a newer cryptographic suite only to not find one that is newer. While this might be an inconvenience, it is one that results in safer ecosystem behavior.

Modern cryptographic algorithms provide a number of tunable parameters and options to ensure that the algorithms can meet the varied requirements of different use cases. For example, embedded systems have limited processing and memory environments and might not have the resources to generate the strongest digital signatures for a given algorithm. Other environments, like financial trading systems, might only need to protect data for a day while the trade is occurring, while other environments might need to protect data for multiple decades. To meet these needs, cryptographic algorithm designers often provide multiple ways to configure a cryptographic algorithm.

Cryptographic library implementers often take the specifications created by cryptographic algorithm designers and specification authors and implement them such that all options are available to the application developers that use their libraries. This can be due to not knowing which combination of features a particular application developer might need for a given cryptographic deployment. All options are often exposed to application developers.

Application developers that use cryptographic libraries often do not have the requisite cryptographic expertise and knowledge necessary to appropriately select cryptographic parameters and options for a given application. This lack of expertise can lead to an inappropriate selection of cryptographic parameters and options for a particular application.

This specification sets the priority of constituencies to protect application developers over cryptographic library implementers over cryptographic specification authors over cryptographic algorithm designers. Given these priorities, the following recommendations are made:

Cryptographic algorithm designers are advised [RFC7696] to minimize the number of options and parameters to as few as possible to ensure that cryptographic library implementers have a more easily auditable security attack surface for their software libraries.
Cryptographic specification authors are advised to, if possible, further minimize the number of options and parameters to as few as possible to ensure cryptographic agility while also keeping the auditable security attack surface for downstream software libraries to a minimum.
Cryptographic library implementers are advised to, if possible, provide known good combinations of options and parameters to application developers. There would ideally be two pre-set default configurations for any algorithmic class, such as Elliptic Curve Digital Signatures, with no ability to fine tune parameters and options when using these pre-sets. Library options can be provided to experts to fine tune their use of the library, use of those options by the general application developer population is to be discouraged.
Application developers are advised to choose from a number of pre-set cryptography library configurations and to avoid modifying cryptographic options and parameters, or using experimental or deprecated cryptography.

The guidance above is meant to ensure that useful cryptographic options and parameters are provided at the lower layers of the architecture while not exposing those options and parameters to application developers who may not fully understand the balancing benefits and drawbacks of each option.

Issue 6: Use of experimental and deprecated cryptography

The VCWG is seeking guidance on adding language to allow the use of experimental or deprecated cryptography. By default, those features will be disabled and will require the application developer to specifically allow use on a per-cryptographic suite basis. There will be requirements for all implementing libraries to throw errors or warnings when deprecated or experimental options are selected without the appropriate override flags.

Section 5.1 Versioning Cryptography Suites emphasized the importance of providing relatively easy to understand information concerning the timeliness of particular cryptographic suite, while section 5.2 Protecting Application Developers further emphasized minimizing the number of options to be specified. Indeed, section 3. Cryptographic Suites lists requirements for cryptographic suites which include detailed specification of algorithm, transformation, hashing, and serialization. Hence, the name of the cryptographic suite does not need to include all this detail, which implies the parametric versioning mentioned in section 5.1 Versioning Cryptography Suites is neither necessary nor desirable.

The recommended naming convention for cryptographic suites is a string composed of a signature algorithm identifier, separated by a hyphen from an option identifier (if the cryptosuite supports incompatible implementation options), followed by a hyphen and designation of the approximate year that the suite was proposed.

For example, the [DI-EDDSA] is based on EdDSA digital signatures, supports two incompatible options based on canonicalization approaches, and was proposed in roughly the year 2022, so it would have two different cryptosuite names: eddsa-rdfc-2022 and eddsa-jcs-2022.

Although the [DI-ECDSA] is based on ECDSA digital signatures, supports the same two incompatible canonicalization approaches as [DI-EDDSA], and supports two different levels of security (128 bit and 192 bit) via two alternative sets of elliptic curves and hashes, it has only two cryptosuite names: ecdsa-rdfc-2019 and ecdsa-jcs-2019. The security level and corresponding curves and hashes are determined from the multi-key format of the public key used in validation.

Cryptographic agility is a practice by which one designs frequently connected information security systems to support switching between multiple cryptographic primitives and/or algorithms. The primary goal of cryptographic agility is to enable systems to rapidly adapt to new cryptographic primitives and algorithms without making disruptive changes to the systems' infrastructure. Thus, when a particular cryptographic primitive, such as the SHA-1 algorithm, is determined to be no longer safe to use, systems can be reconfigured to use a newer primitive via a simple configuration file change.

Cryptographic agility is most effective when the client and the server in the information security system are in regular contact. However, when the messages protected by a particular cryptographic algorithm are long-lived, as with Verifiable Credentials, and/or when the client (holder) might not be able to easily recontact the server (issuer), then cryptographic agility does not provide the desired protections.

Cryptographic layering is a practice where one designs rarely connected information security systems to employ multiple primitives and/or algorithms at the same time. The primary goal of cryptographic layering is to enable systems to survive the failure or one or more cryptographic algorithms or primitives without losing cryptographic protection on the payload. For example, digitally signing a single piece of information using RSA, ECDSA, and Falcon algorithms in parallel would provide a mechanism that could survive the failure of two of these three digital signature algorithms. When a particular cryptographic protection is compromised, such as an RSA digital signature using 768-bit keys, systems can still utilize the non-compromised cryptographic protections to continue to protect the information. Developers are urged to take advantage of this feature for all signed content that might need to be protected for a year or longer.

This specification provides for both forms of agility. It provides for cryptographic agility, which allows one to easily switch from one algorithm to another. It also provides for cryptographic layering, which allows one to simultaneously use multiple cryptographic algorithms, typically in parallel, such that any of those used to protect information can be used without reliance on or requirement of the others, while still keeping the digital proof format easy to use for developers.

At times, it is beneficial to transform the data being protected during the cryptographic protection process. Such "in-line" transformation can enable a particular type of cryptographic protection to be agnostic to the data format it is carried in. For example, some Data Integrity cryptographic suites utilize RDF Dataset Canonicalization [RDF-CANON] which transforms the initial representation into a canonical form [N-QUADS] that is then serialized, hashed, and digitally signed. As long as any syntax expressing the protected data can be transformed into this canonical form, the digital signature can be verified. This enables the same digital signature over the information to be expressed in JSON, CBOR, YAML, and other compatible syntaxes without having to create a cryptographic proof for every syntax.

Being able to express the same digital signature across a variety of syntaxes is beneficial because systems often have native data formats with which they operate. For example, some systems are written against JSON data, while others are written against CBOR data. Without transformation, systems that process their data internally as CBOR are required to store the digitally signed data structures as JSON (or vice-versa). This leads to double-storing data and can lead to increased security attack surface if the unsigned representation stored in databases accidentally deviates from the signed representation. By using transformations, the digital proof can live in the native data format to help prevent otherwise undetectable database drift over time.

This specification is designed to avoid requiring the duplication of signed information by utilizing "in-line" data transformations. Application developers are urged to work with cryptographically protected data in the native data format for their application and not separate storage of cryptographic proofs from the data being protected. Developers are also urged to regularly confirm that the cryptographically protected data has not been tampered with as it is written to and read from application storage.

Some transformations, such as RDF Dataset Canonicalization [RDF-CANON], have mitigations for input data sets that can be used by attackers to consume excessive processing cycles. This class of attack is called dataset poisoning, and all modern RDF Dataset canonicalizers are required to detect these sorts of bad inputs and halt processing. The test suites for RDF Dataset Canonicalization includes such poisoned datasets to ensure that such mitigations exist in all conforming implementations. Generally speaking, cryptographic suite specifications that use transformations are required to mitigate these sorts of attacks, and implementers are urged to ensure that the software libraries that they use enforce these mitigations. These attacks are in the same general category as any resource starvation attack, such as HTTP clients that deliberately slow connections, thus starving connections on the server. Implementers are advised to consider these sorts of attacks when implementing defensive security strategies.

Issue 7: Collision-resistant canonicalization requirements

The VCWG is seeking feedback on normative language that cryptographic suite implementers need to follow to ensure that they do not utilize data transformation mechanisms that can map to the same output. That is, given different inputs for canonicalization scheme #1 and canonicalization scheme #2, they must not produce the same output value. As an analogy, this is the same requirement for cryptographic hashing mechanisms and is why those schemes are designed to be collision resistant. Cryptographic canonicalization mechanisms have the same requirement. At present, this isn't a problem because the three expected canonicalization schemes — the Universal RDF Dataset Canonicalization Algorithm 2015 [RDF-CANON], JSON Canonicalization Scheme [RFC8785], and a theoretical future base-encoding canonicalization — have entirely different outputs.

Issue 8: Avoiding the pitfalls of XML Canonicalization

The VCWG is seeking feedback on whether to explain why modern canonicalization schemes are simpler than the far more complex XML Canonicalization schemes of the early 2000s. Some readers seem to be under the impression that all canonicalization is difficult and has to be avoided at all costs (including costs to application developers). The WG would like to understand if it would be helpful to include a section explaining why some simpler data syntaxes (such as JSON) are easier to canonicalize than more complex data syntaxes (such as XML).

The data that is protected by any data integrity proof is the transformed data. Transformed data is generated by a transformation algorithm that is specified by a particular cryptosuite. This protection mechanism differs from some more traditional digital signature mechanisms that do not perform any sort of transformation on the input data. The benefits of transformation are detailed in Section 5.5 Transformations.

For example, cryptosuites such as ecdsa-jcs-2019 and eddsa-jcs-2022 use the JSON Canonicalization Scheme (JCS) to transform the data to canonicalized JSON, which is then cryptographically hashed and digitally signed. One benefit of this approach is that adding or removing formatting characters that do not impact the meaning of the information being signed, such as spaces, tabs, and newlines, does not invalidate the digital signature. More traditional digital signature mechanisms do not have this capability.

Other cryptosuites such as ecdsa-rdfc-2019 and eddsa-rdfc-2022 use RDF Dataset Canonicalization to transform the data to canonicalized N-Quads [N-QUADS], which is then cryptographically hashed and digitally signed. One benefit of this approach is that the cryptographic signature is portable to a variety of different syntaxes, such as JSON, YAML, and CBOR, without invalidating the signature. More traditional cryptographic signature mechanisms do not have this capability.

Implementers and developers are urged to not trust information that contains a data integrity proof unless the proof has been verified and the verified data is provided in a return value from a software library that has confirmed that all data returned has been successfully protected.

The inspectability of application data has effects on system efficiency and developer productivity. When cryptographically protected application data, such as base-encoded binary data, is not easily processed by application subsystems, such as databases, it increases the effort of working with the cryptographically protected information. For example, a cryptographically protected payload that can be natively stored and indexed by a database will result in a simpler system that:

benefits from utilizing existing industry-standard database features with no changes to the protected information,
avoids the complexity of duplicating data where one copy of the data preserves the message and digital signature, while the other copy only stores and indexes the message and is what drives system behaviour,
avoids the complexity of bespoke solutions that have to structurally modify the protected information, such as serializing and deserializing nested digitally signed data that has multiple nested base-encoded payloads.

Similarly, a cryptographically protected payload that can be processed by multiple upstream networked systems increases the ability to properly layer security architectures. For example, if upstream systems do not have to repeatedly decode the incoming payload, it increases the ability for a system to distribute processing load by specializing upstream subsystems to actively combat attacks. While a digital signature needs to always be checked before taking substantive action, other upstream checks can be performed on transparent payloads — such as identifier-based rate limiting, signature expiration checking, or nonce/challenge checking — to reject obviously bad requests.

Additionally, if a developer is not able to easily view data in a system, the ability to easily audit or debug system correctness is hampered. For example, requiring application developers to cut-and-paste base-encoded application data makes development more challenging and increases the chances that obvious bugs will be missed because every message needs to go through a manually operated base-decoding tool.

There are times, however, where the correct design decision is to make data opaque. Data that does not need to be processed by other application subsystems, as well as data that does not need to be modified or accessed by an application developer, can be serialized into opaque formats. Examples include digital signature values, cryptographic key parameters, and other data fields that only need to be accessed by a cryptographic library and need not be modified by the application developer. There are also examples where data opacity is appropriate when the underlying subsystem does not expose the application developer to the underlying complexity of the opaque data, such as databases that perform encryption at rest. In these cases, the application developer continues to develop against transparent application data formats while the database manages the complexity of encrypting and decrypting the application data to and from long-term storage.

This specification strives to provide an architecture where application data remains in its native format and is not made opaque, while other cryptographic data, such as digital signatures, are kept in their opaque binary encoded form. Cryptographic suite implementers are urged to consider appropriate use of data opacity when designing their suites, and to weigh the design trade-offs when making application data opaque versus providing access to cryptographic data at the application layer.

Issue 9

Implementers must ensure that a verification method is bound to a particular controller by going from the verification method to the controller document, and then ensuring that the controller document also contains the verification method.

When an implementation is verifying a proof, it is imperative that it verify not only that the verification method used to generate the proof is listed in the controller document, but also that it was intended to be used to generate the proof that is being verified. This process is known as "verification relationship validation".

The process of validating a verification relationship is outlined in Section 3.3 Retrieve Verification Method of the Controller Documents 1.0 specification.

This process is used to ensure that cryptographic material, such as a private cryptographic key, is not misused by application to an unintended purpose. An example of cryptographic material misuse would be if a private cryptographic key meant to be used to issue a Verifiable Credential was instead used to log into a website (that is, for authentication). Not checking a verification relationship is dangerous because the restriction and protection profile for some cryptographic material could be determined by its intended use. For example, some applications could be trusted to use cryptographic material for only one purpose, or some cryptographic material could be more protected, such as through storage in a hardware security module in a data center versus as an unencrypted file on a laptop.

When an implementation is verifying a proof, it is imperative that it verify that the proof purpose match the intended use.

This process is used to ensure that proofs are not misused by an application for an unintended purpose, as this is dangerous for the proof creator. An example of misuse would be if a proof that stated its purpose was for securing assertions in verifiable credentials was instead used for authentication to log into a website. In this case, the proof creator attached proofs to any number of verifiable credentials that they expected to be distributed to an unbounded number of other parties. Any one of these parties could log into a website as the proof creator if the website erroneously accepted such a proof as authentication instead of its intended purpose.

The way in which a transformation, such as canonicalization, is performed can affect the security characteristics of a system. Selecting the best canonicalization mechanisms depends on the use case. Often, the simplest mechanism that satisfies the desired security requirements is the best choice. This section attempts to provide simple guidance to help implementers choose between the two main canonicalization mechanisms referred to in this specification, namely JSON Canonicalization Scheme [RFC8785] and RDF Dataset Canonicalization [RDF-CANON].

If an application only uses JSON and does not depend on any form of RDF semantics, then using a cryptography suite that uses JSON Canonicalization Scheme [RFC8785] is an attractive approach.

If an application uses JSON-LD and needs to secure the semantics of the document, then using a cryptography suite that uses RDF Dataset Canonicalization [RDF-CANON] is an attractive approach.

Implementers are also advised that other mechanisms that perform no transformations are available, that secure the data by wrapping it in a cryptographic envelope instead of embedding the proof in the data, such as JWTs [RFC7519] and CWTs [RFC8392]. These approaches have simplicity advantages in some use cases, at the expense of some of the benefits provided by the approach detailed in this specification.

One of the algorithmic processes used by this specification is canonicalization, which is a type of transformation. Canonicalization is the process of taking information that might be expressed in a variety of semantically equivalent ways as input, and expressing all output in a single way, called a "canonical form".

The security of a resulting data integrity proof that utilizes canonicalization is highly dependent on the correctness of the algorithm. For example, if a canonicalization algorithm converts two inputs that have different meanings into the same output, then the author's intentions can be misrepresented to a verifier. This can be used as an attack vector by adversaries.

Additionally, if semantically relevant information in an input is not present in the output, then an attacker could insert such information into a message without causing proof verification to fail. This is similar to another transformation that is commonly used when cryptographically signing messages: cryptographic hashing. If an attacker is able to produce the same cryptographic hash from a different input, then the cryptographic hash algorithm is not considered secure.

Implementers are strongly urged to ensure proper vetting of any canonicalization algorithms to be used for transformation of input to a hashing process. Proper vetting includes, at a minimum, association with a peer reviewed mathematical proof of algorithm correctness; multiple implementations and vetting by experts in a standards setting organization is preferred. Implementers are strongly urged not to invent or use new mechanisms unless they have formal training in information canonicalization and/or access to experts in the field who are capable of producing a peer reviewed mathematical proof of algorithm correctness.

This specification is designed in such a way that no network requests are required when verifying a proof on a conforming secured document. Readers might note, however, that JSON-LD contexts and verification methods can contain URLs that might be retrieved over a network connection. This concern exists for any URL that might be loaded from the network during or after verification.

To the extent possible, implementers are urged to permanently or aggressively cache such information to reduce the attack surface on an implementation that might need to fetch such URLs over the network. For example, caching techniques for JSON-LD contexts are described in Section 2.4 Contexts and Vocabularies, and some verification methods, such as did:key [DID-KEY], do not need to be fetched from the network at all.

When it is not possible to use cached information, such as when a specific HTTP URL-based instance of a verification method is encountered for the first time, implementers are cautioned to use defensive measures to mitigate denial-of-service attacks during any process that might fetch a resource from the network.

Since the technology to secure documents described by this specification is generalized in nature, the security implications of its use might not be immediately apparent to readers. To understand the sort of security concerns one might need to consider in a complete software system, implementers are urged to read about how this technology is used in the verifiable credentials ecosystem [VC-DATA-MODEL-2.0]; see the section on Verifiable Credential Security Considerations for more information.

This section is non-normative.

Sections 2.1.1 Proof Sets and 2.1.2 Proof Chains describe how multiple proofs can be expressed in a secured data document; that is, instead of a single proof included in the secured data document, one can express multiple proofs in an array as shown in Example 5 and Example 6. The elements of this array are members of a proof set and, optionally, a proof chain. The purpose of this section is to explain the intended use of each of these features and, in particular, their differing security properties. These differing security properties lead to differences in the processing in section 4.3 Add Proof Set/Chain.

This section represents secured data documents, including their proofs, in an abbreviated manner so that the important security properties can be observed.

Consider a scenario with three signatories: a CEO, a CFO, and a VP of Engineering. Each will need to have a public key and secret key pair for signing a document. We denote the secret/public keys of each of these signatories by secretCEO/publicCEO, secretCFO/publicCFO, and secretVPE/publicVPE, respectively.

When constructing a proof set where each of the signatories signs an inputDocument without concern, we construct a proof symbolically as:

Example 8: Symbolic expression of how a proof is created

{
  "type": "DataIntegrityProof",
  "cryptosuite": "eddsa-jcs-2022",
  "created": "2023-03-05T19:23:24Z",
  "proofPurpose": "assertionMethod",
  "verificationMethod": publicCEO,
  "proofValue": signature(secretCEO, inputDocument)
}

Where publicCEO is used as a placeholder for a reference that resolves to the CEO's public key and signature(secretKey, inputDocument) denotes the computation of a digital signature by a particular data integrity cryptosuite using a particular secret key over a particular document. The type, cryptosuite, created, and proofPurpose attributes do not factor into our discussion so we will omit them. In particular, below we show all the proofs in a proof set on a document that has been signed by the VP of Engineering, the CFO, and the CEO:

Example 9: Symbolic expression of a proof set

{
  // Remainder of secured data document not shown (above)
  "proof": [{
    "verificationMethod": publicVPE,
    "proofValue": signature(secretVPE, inputDocument)
  }, {
    "verificationMethod": publicCFO,
    "proofValue": signature(secretCFO, inputDocument)
  }, {
    "verificationMethod": publicCEO,
    "proofValue": signature(secretCEO, inputDocument)
  }]
}

A holder or any other intermediary receiving a secured data document containing a proof set is able to remove any of the proof values within the set prior to passing it on to another entity and the secured data document will still verify. This might or might not have been the intent. For the signatories sending a birthday card to a valued employee, using a proof set is probably fine. If we are trying to model a business process where approvals ascend the company hierarchy, this would not be ideal, since any intermediary could remove signatures from the proof set and still have it verify; for instance, in the example below, it looks like the CFO and CEO approved something without the VP of Engineering's concurrence.

Example 10: Removal of a signature in a proof set

{
  // Remainder of secured data document not shown (above)
  "proof": [{
    "verificationMethod": publicCFO,
    "proofValue": signature(secretCFO, inputDocument)
  }, {
    "verificationMethod": publicCEO,
    "proofValue": signature(secretCEO, inputDocument)
  }]
}

It is possible to introduce a dependency between proofs in a proof set by setting the id property of each proof such that another proof can reference it. In other words, a dependent proof will be referenced by other relying proofs by using the previousProof property. Such dependency chains can have arbitrary depth. The intent of such a proof chain is to model an approval chain in a business process or a notary witnessing analog signatures.

The examples below demonstrate how a proof chain can be constructed when the VP of Engineering signs off on the document first; based on the VP of Engineering's signature and a review, the CFO then signs off on the document; and finally, based on both prior signatures and a review, the CEO signs off on the document. Since others will be referring to the VP of Engineering's signature, we need to add an id to the proof. First the VP of Engineering signs the input document:

Example 11: Proof chain containing first proof with `id` property set

{
  // Remainder of secured data document not shown (above)
  "proof": {
    "id": "urn:proof-1",
    "verificationMethod": publicVPE,
    "proofValue": signature(secretVPE, inputDocument)
  }
}

Next, the CFO receives the document, verifies that the VP of Engineering signed it, and signs it based on a review and on the signature of the VP of Engineering. For this, we need to set up the proof chain by indicating a dependency on the proof in the document just received. We do this by setting the previousProof property of the second proof to the value urn:proof-1, which "binds" the second proof to the first proof, which is then signed. The following example shows how the dependency on the first proof is created:

Example 12: Proof chain containing two proofs

{
  // Remainder of secured data document not shown (above)
  "proof": [{
    "id": "urn:proof-1",
    "verificationMethod": publicVPE,
    "proofValue": signature(secretVPE, inputDocument)
  }, {
    "id": "urn:proof-2",
    "verificationMethod": publicCFO,
    "previousProof": "urn:proof-1",
    "proofValue": signature(secretCFO, inputDocumentWithProof1)
  }]
}

Now, when the CEO verifies the received secured data document with the above proof chain, they will check that the CFO signed based on the signature of the VP of Engineering. First, they will check the proof with an id property whose value is urn:proof-1 against the public key of the VP of Engineering. Note that this proof is over the original document.

Next, the CEO will check the proof with an id property whose value is urn:proof-2 against the public key of the CFO. However, to make sure that the CFO signed the document with proof that the VP of Engineering had already signed, we verify this proof over the combination of the document and urn:proof-1. If verification is successful, the CEO signs, producing a proof over the document which includes urn:proof-1 and urn:proof-2. The final proof chain looks like this:

Example 13: Proof chain containing three proofs

{
  // Remainder of secured data document not shown (above)
  "proof": [{
    "id": "urn:proof-1",
    "verificationMethod": publicVPE,
    "proofValue": signature(secretVPE, inputDocument)
  }, {
    "id": "urn:proof-2",
    "verificationMethod": publicCFO,
    "previousProof": "urn:proof-1",
    "proofValue": signature(secretCFO, inputDocumentWithProof1)
  }, {
    "id": "urn:proof-3",
    "verificationMethod": publicCEO,
    "previousProof": "urn:proof-2",
    "proofValue": signature(secretCEO, inputDocumentWithProof2)
  }]
}

The recipient of this secured data document then validates it in a similar way, checking each proof in the chain.

Verifiable Credential Data Integrity 1.0

Securing the Integrity of Verifiable Credential Data

Abstract

Status of This Document

1. Introduction

1.1 How it Works

1.2 Design Goals and Rationale

1.3 Conformance

1.4 Terminology

2. Data Model

2.1 Proofs

2.1.1 Proof Sets

2.1.2 Proof Chains

2.1.3 Proof Graphs

2.2 Proof Purposes

2.3 Resource Integrity

2.4 Contexts and Vocabularies

2.4.1 Validating Contexts

2.4.2 Context Injection

2.4.3 Securing Data Losslessly

2.4.4 Datatypes

2.4.4.1 The cryptosuiteString Datatype

2.4.4.2 The multibase Datatype

2.5 Relationship to Linked Data

2.6 Relationship to Verifiable Credentials

3. Cryptographic Suites

3.1 DataIntegrityProof

4. Algorithms

4.1 Processing Model

4.2 Add Proof

4.3 Add Proof Set/Chain

4.4 Verify Proof

4.5 Verify Proof Sets and Chains

4.6 Context Validation

4.7 Processing Errors

5. Security Considerations

5.1 Versioning Cryptography Suites

5.2 Protecting Application Developers

5.3 Conventions for Naming Cryptography Suites

5.4 Agility and Layering

5.5 Transformations

5.6 Protected Information

5.7 Data Opacity

5.8 Verification Method Binding

5.9 Verification Relationship Validation

5.10 Proof Purpose Validation

5.11 Canonicalization Method Security

5.12 Canonicalization Method Correctness

5.13 Network Requests

5.14 Other Security Considerations

6. Privacy Considerations

6.1 Unlinkability

6.2 Selective Disclosure

6.3 Previous Proofs

6.4 Fingerprinting Network Requests

6.5 Canonicalization Method Privacy

6.6 Other Privacy Considerations

7. Accessibility Considerations

7.1 Presenting Time Values

A. Understanding Proof Sets and Proof Chains

B. Revision History

C. Acknowledgements

D. References

D.1 Normative references

D.2 Informative references

2.4.4.1 The `cryptosuiteString` Datatype

2.4.4.2 The `multibase` Datatype