Option A: Permanently Deidentified

Replace existing text with the following definition and non-normative explanation section.

Data is permanently de-identified when there exists a high level of confidence that no human subject of the data can be identified, directly or indirectly (e.g., via association with an identifier, user agent, or device), by that data alone or in combination with other retained or available information.

Separate, non-normative section

In this specification the term ‘permanently de-identified’ is used for data that has passed out of the scope of this specification and can not, and will never, come back into scope. The organization that performs the de-identification needs to be confident that the data can never again identify the human subjects whose activity contributed to the data. That confidence may result from ensuring or demonstrating that it is no longer possible to:

• isolate some or all records which correspond to a device or user;
• link two or more records (either from the same database or different databases), concerning the same device or user;
• deduce, with significant probability, information about a device or user.

Regardless of the de-identification approach, unique keys can be used to correlate records within the de-identified dataset, provided the keys do not exist and cannot be derived outside the de-identified dataset and have no meaning outside the de-identified dataset (i.e. no mapping table can exist that links the original identifiers to the keys in the de-identified dataset.)

In the case of records in such data that relate to a single user or a small number of users, usage and/or distribution restrictions are advisable; experience has shown that such records can, in fact, sometimes be used to identify the user(s) despite the technical measures that were taken to prevent re-identification. It is also a good practice to disclose (e.g. in the privacy policy) the process by which de-identification of these records is done, as this can both raise the level of confidence in the process, and allow for for feedback on the process. The restrictions might include, for example:

• Technical safeguards that prohibit re-identification of de-identified data and/or merging of the original tracking data and de-identified data;
• Business processes that specifically prohibit re-identification of de-identified data and/or merging of the original tracking data and de-identified data;
• Business processes that prevent inadvertent release of either the original tracking data or de-identified data;
• Administrative controls that limit access to both the original tracking data and de-identified data.

If you have an objection to this option, please describe your objection, with clear and specific reasoning.

Details

Option B: Expert review or safe harbor

Replace existing text with the following definition and non-normative explanation:

For the purpose of this specification, tracking data may be de-identified using one of two methods:

1. Expert Review: A qualified statistical or scientific expert concludes, through the use of accepted analytic techniques, that the risk the information could be used alone, or in combination with other reasonably available information, to identify a user is very small.

2. Safe Harbor: Removal of the following fields and/or data types from the tracking data:

• Cleanse URLs to remove end user information such as names, IDs, or account specific information
• Any geographic information that represents a granularity less than zip code
• Date information specific to the end user (e.g. DOB, graduation, anniversary, etc.) Transaction dates (purchases, registration, shipping, etc.) specific to the end user can be retained as long as timestamp information is removed or obfuscated
• User age – note that age group information (e.g. 30-40) can be kept so long as the ages are expanded to year of birth at a minimum. Multiple year age bands are preferred.
• Direct contact elements such as telephone numbers, email addresses, social network usernames, or other public “handles” that uniquely identify a user on a given service
• Social security numbers or other government issued identifiers (e.g. driver’s license number, registration numbers, tax id numbers, license plate information)
• Account numbers, membership numbers, or other static identifiers that can be used to identify the user on another site or service or to a place of business or other organization
• Full IP addresses and/or remote hostnames – may be converted to representative geolocation (no more granular than zip code) * Biometric information, including video or images of the end user, voice prints/audio recording information

In addition to the removal of the above information, the de-identifying entity must not have actual knowledge that the remaining information could be used alone or in combination with other reasonably available information to identify an individual who is subject of the information.

Further, the de-identifying entity must implement:

• Technical safeguards that prohibit re-identification of de-identified data and/or merging of the original tracking data and de-identified data
• Business processes that specifically prohibit re-identification of de-identified data and/or merging of the original tracking data and de-identified data
• Business processes that prevent inadvertent release of either the original tracking data or de-identified data
• Administrative controls that limit access to both the original tracking data and de-identified data

If third parties will have access to the de-identified data, the de-identifying entity must have contractual protections in place that require the third parties (and their agents or affiliates) to:

• Appropriately protect the data
• Not attempt to re-identify the data
• Only use the data for purposes specified by first party

Regardless of the de-identification approach, unique keys can be used to correlate records within the de-identified dataset, provided the keys do not exist outside the de-identified dataset and/or have no meaning outside the de-identified dataset (i.e. no mapping table can exist that links the original identifiers to the keys in the de-identified dataset.)

A de-identified dataset becomes irrevocably de-identified if the algorithm information used to generate the unique identifiers (e.g. encryption key(s) or cryptographic hash “salts”) is destroyed after the data is de-identified.

Separate section

Request data sent from user agents can contain information that could potentially be used to identify end users. Such data must be de-identified prior to being used for purposes not listed under permitted uses. While data de-identification does not guarantee complete anonymity, it greatly reduces the risk that a given end user can be re-identified.

Regardless of the method used (Expert Review or Safe Harbor), the de-identifying entity should document the processes it uses for de-identification and any instances where it has implemented de-identification techniques. The entity should regularly review the processes and implementation instances to make sure the appropriate methods are followed.

Both tracking data and de-identified data MUST be appropriately protected using industry best practices, including:

• Access by authorized personnel only
• Rule of Least Privilege
• Use of secure transfer/access protocols
• Secure destruction of data once it is no longer needed

The de-identification and cleansing of URL data is particularly important, since the variety and format of identifying information will vary. Considerations for cleansing URL information:

• Truncation to URL domain only where possible
• Where path and query string information must be retained, key-value information should be scrubbed for known proprietary data types as well as data that matches patterns for known PII formats (e.g. telephone numbers, email addresses, etc.)

If you have an objection to this option, please describe your objection, with clear and specific reasoning.

Details