http://www.cs.cmu.edu/~katrina

AG 1, AG 4, RG1, MMCI, AG 3, AG 5, SWS   Info

AG Audience

English

30 Minutes

Saarbrücken

We consider the problem of designing approximation algorithms for
discrete optimization problems over private data sets, in the
framework of differential privacy (which formalizes the idea of
protecting the privacy of individual input elements). Our results show
that for several commonly studied combinatorial optimization problems,
it is possible to release approximately optimal solutions while
preserving differential privacy; this is true even in cases where it
is impossible under cryptographic definitions of privacy to release
even approximations to the *value* of the optimal solution.

In this talk, we will focus on the private vertex cover problem, where
a set of edges must each be covered by a vertex, without disclosing
the presence or absence of any particular edge. We show an efficient,
differentially-private 2-approximation to the value, and a factor (2 +
16/epsilon)-approximate solution (where epsilon is the differential
privacy parameter, controlling the amount of information disclosure).
We also present a simple lower bound arguing that an Omega(1/epsilon)
factor dependence is natural and necessary.

We will also briefly discuss a variety of other combinatorial
problems, and implications of this work for mechanism design in
submodular maximization problems.

Much of this work was done while the speaker was visiting Microsoft
Research. This work is joint with Anupam Gupta and Aaron Roth (both
at Carnegie Mellon), and Frank McSherry and Kunal Talwar (both at
Microsoft Research).

Evangelia Pyrga

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