ACTION-371: text defining de-identified data

My thanks to Dan for providing this.

This text is based on the language in the FTC's 2012 privacy report, at http://ftc.gov/os/2012/03/120326privacyreport.pdf, page 21.

Peter



C. William O'Neill Professor of Law
    Ohio State University
240.994.4142
www.peterswire.net

From: Dan Auerbach <dan@eff.org<mailto:dan@eff.org>>
Date: Monday, March 4, 2013 12:23 PM
To: "public-tracking@w3.org<mailto:public-tracking@w3.org>" <public-tracking@w3.org<mailto:public-tracking@w3.org>>
Subject: text defining de-identified data
Resent-From: <public-tracking@w3.org<mailto:public-tracking@w3.org>>
Resent-Date: Monday, March 4, 2013 12:24 PM


Hi everyone,

I wanted to pass along some text regarding de-identification that Peter asked me prepare based largely on the FTC language that was discussed at the f2f, additionally including what I consider important non-normative text for guidance. I suspect the "example"/"remark" language within the non-normative text below are non-standard W3C terms, and am happy to take guidance from members of the group more familiar with W3C to amend this language appropriately to fit W3C style.

Best,
Dan

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Normative text:

Data can be considered sufficiently de-identified to the extent that a company:

 1.  sufficiently deletes, scrubs, aggregates, anonymizes and otherwise manipulates the data in order to achieve a reasonable level of justified confidence that the data cannot be used to infer any information about, or otherwise be linked to, a particular consumer, device or user agent;

 2.  publicly commits not to try to re-identify the data, except in order to test the soundness of the de-identified data; and

 3.  contractually prohibits downstream recipients from trying to re-identify the data.

Non-normative text:

Example 1. Hashing a pseudonym such as a cookie string does NOT provide sufficient de-identification for an otherwise rich data set, since there are many ways to re-identify individuals based on pseudonymous data.

Example 2. In many cases, keeping only high-level aggregate data, such as the total number of visitors of a website each day broken down by country (discarding data from countries without many visitors) would be considered sufficiently de-identified.

Example 3. Deleting data is always a safe and easy way to achieve de-identification.

Remark 1. De-identification is a property of data. If data can be considered de-identified according to the “reasonable level of justified confidence” clause of (1), then no data manipulation process needs to take place in order to satisfy the requirements of (1).

Remark 2. There are a diversity of techniques being researched and developed to de-identify data sets (e.g. [1][2]), and companies are encouraged to explore and innovate new approaches to fit their needs.

Remark 3. It is a best practice for companies to perform “penetration testing” by having an expert with access to the data attempt to re-identify individuals or disclose attributes about them. The expert need not actually identify or disclose the attribute of an individual, but if the expert demonstrates how this could plausibly be achieved by joining the data set against other public data sets or private data sets accessible to the company, then the data set in question should no longer be considered sufficiently de-identified and changes should be made to provide stronger anonymization for the data set.

[1] https://research.microsoft.com/pubs/116123/dwork_cacm.pdf

[2] http://www.cs.purdue.edu/homes/ninghui/papers/t_closeness_icde07.pdf

--
Dan Auerbach
Staff Technologist
Electronic Frontier Foundation
dan@eff.org<mailto:dan@eff.org>
415 436 9333 x134