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Learning Statistics with Privacy, aided by the Flip of a Coin
Thursday, October 30, 2014
Posted by Úlfar Erlingsson, Tech Lead Manager, Security Research
(Cross-posted on the
Chromium Blog
and the
Google Online Security Blog
)
At Google, we are constantly trying to improve the techniques we use to
protect our users' security and privacy
. One such project, RAPPOR (Randomized Aggregatable Privacy-Preserving Ordinal Response), provides a new state-of-the-art, privacy-preserving way to learn software statistics that we can use to better safeguard our users’ security, find bugs, and improve the overall user experience.
Building on the concept of
randomized response
, RAPPOR enables learning statistics about the behavior of users’ software while guaranteeing client privacy. The guarantees of
differential privacy
, which are widely accepted as being the
strongest form of privacy
, have almost never been used in practice despite
intense research in academia
. RAPPOR introduces a practical method to achieve those guarantees.
To understand RAPPOR, consider the following example. Let’s say you wanted to count how many of your online friends were dogs, while respecting the maxim that,
on the Internet, nobody should know you’re a dog
. To do this, you could ask each friend to answer the question “Are you a dog?” in the following way. Each friend should flip a coin in secret, and answer the question truthfully if the coin came up heads; but, if the coin came up tails, that friend should always say “Yes” regardless. Then you could get a good estimate of the true count from the greater-than-half fraction of your friends that answered “Yes”. However, you still wouldn’t know which of your friends was a dog: each answer “Yes” would most likely be due to that friend’s coin flip coming up tails.
RAPPOR builds on the above concept, allowing software to send reports that are effectively indistinguishable from the results of random coin flips and are free of any unique identifiers. However, by aggregating the reports we can learn the common statistics that are shared by many users. We’re currently testing the use of RAPPOR in Chrome, to learn statistics about how
unwanted software
is
hijacking
users’ settings.
We believe that RAPPOR has the potential to be applied for a number of different purposes, so we're making it freely available for all to use. We'll continue development of RAPPOR as a standalone
open-source project
so that anybody can inspect and test its reporting and analysis mechanisms, and help develop the technology. We’ve written up the technical details of RAPPOR in a
report
that will be published next week at the
ACM Conference on Computer and Communications Security
.
We’re encouraged by the
feedback
we’ve received so far from academics and other stakeholders, and we’re looking forward to additional comments from the community. We hope that everybody interested in preserving user privacy will review the technology and share their feedback at
rappor-discuss@googlegroups.com
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