Privacy as plausible deniability?

As I was flying to the NSDI PC meeting this week I was catching up on reading and came across an article on privacy in the Atlantic that (to my surprise) pushed nearly the same perspective on privacy that we studied in a paper a year or so ago… Privacy as plausable deniability.

The idea is that hacks, breaches, monitoring behavior, etc. are so common and hard to avoid that relying on tools from crypto or differential privacy isn’t really enough.  Instead, if someone really cares about privacy they probably need to take that into account in their actions.  For example, you can assume that google/facebook/etc. are observing your behavior online and that this is impacting prices, advertisements, etc. Tools from privacy, encryption, etc. can’t really help with this.  However, tools that add “fake” traffic can.  If an observer knows that you are using such a tool then you always have plausible deniability about any observed behavior, and if these are chosen carefully, then they can counter the impact of personalized ads, pricing, etc.  There are now companies such as “Plausible Deniability LLC” that do exactly this!

On the research front, we looked at this in the context of the following question: If a consumer knows that their behavior is being observed and cares about privacy, can the observer infer the true preferences of the consumer?  Our work gives a resounding “no”.  Using tools from revealed preference theory, we show that the observer not only cannot learn, but that every set of observed choices can be “explained” as consistent with any underlying utility function from the consumer.  Thus, the consumer can always maintain plausible deniability.

If you want to see the details, check it out here!   And, note that the lead author (Rachel Cummings) is on the job market this year!

P.S. The NSDI PC meeting was really stimulating!  It’s been a while since I had the pleasure of being on a “pure systems” PC, and it was great to see quite a few rigorous/mathematical papers be discussed and valued.  Also, it was quite impressive to see how fair and thorough the discussions were.  Congrats to Aditya and Jon on running a great meeting!

Simons Workshop on Big Data and Differential Privacy

I recently returned from a workshop on Big Data and Differential Privacy, hosted by the Simons Institute for the Theory of Computing, at Berkeley.

Differential privacy is a rigorous notion of database privacy intended to give meaningful guarantees to individuals whose personal data are used in computations, where “computations” is quite broadly understood—statistical analyses, model fitting, policy decisions, release of “anonymized” datasets,…

Privacy is easy to get wrong, even when data-use decisions are being made by well-intentioned, smart people. There are just so many subtleties, and it is impossible to fully anticipate the range of attacks and outside information an adversary might use to compromise the information you choose to publish. Thus, much of the power of differential privacy comes from the fact that it gives guarantees that hold up without making any assumptions about the attacks the adversary might use, her computational power, or any outside information she might acquire. It also has elegant composition properties (helping us understand how privacy losses accumulate over multiple computations).

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