RE: de-identification text for Wednesday's call

Dan,

I agree with the core definition (for the most part - will offer a few tweaks soon) but strongly disagree with all of your non-normative "examples" ("remarks" are fine although a bit too strongly stated).  I would recommend the examples be deleted and a small group of us will come back with alternative language next week.  As the HIPPA examples and others we've discussed call out, any de-identification approach will be risk-based - meaning the party developing their mix of technical, administrative and operational controls is responsible for the ultimate outcome.  A unique replacement for actual operational identifiers for ones that serve purely for analytical purposes paired with strong key access controls, employee education, logging, etc. should be more than able to meet the bar we're setting here.

Thank you,
Shane

From: Dan Auerbach [mailto:dan@eff.org]
Sent: Tuesday, April 02, 2013 1:21 AM
To: public-tracking@w3.org
Subject: de-identification text for Wednesday's call

Hi everyone,

Given that de-identification is on the agenda for Wednesday, I wanted to send out the current state of the de-identification text. No changes to normative text were made since the ending point of the last email thread. I made some small tweaks in order to tighten up the non-normative language, though nothing has conceptually changed.

We are also putting a pin in the issue of requirements and commitments that a DNT-compliant entity must make with respect to de-identification. I think such a specific commitment is warranted, but we agreed to have that discussion separately.

Thanks again to everyone for the feedback,
Dan

Normative text:

Data can be considered sufficiently de-identified to the extent that it has been deleted, modified, aggregated, anonymized or otherwise manipulated in order to achieve a reasonable level of justified confidence that the data cannot reasonably be used to infer information about, or otherwise be linked to, a particular user, user agent, or device.

Non-normative text:

Example 1. In general, using unique or near-unique pseudonymous identifiers to link records of a particular user, user agent, or device within a large data set does NOT provide sufficient de-identification. Even absent obvious identifiers such as names, email addresses, or zip codes, there are many ways to gain information about individuals based on pseudonymous data.

Example 2. In general, keeping only high-level aggregate data across a small number of dimensions, 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 [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 "privacy 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