Privacy Meets Game Theory and Economics

2 Example: Pricing of an Online Cartoon (digital good, unlimited supply)

3 Maximizing Revenue t=  [0,1] titi n p OPT What if I bid 0.5 instead? p p

The Implementation Challenge tntn t n-1  t3t3 t2t2 t1t1 t= Player i does not Prefer M(t’) over M(t) MM Unilateral Deviation by player i M(t) M(t’) tntn t n-1  t3t3 t2t2 t’ 1 t’=

5 The Differential Privacy challenge Dwork, McSherry, Nissim, Smith bnbn b n-1  b3b3 b2b2 b1b1 b= distributions are ε-close AA Neighboring: One entry modified A(b) A(b’) bnbn b n-1  b3b3 b2b2 b’ 1 b’=

6 What we Know [NST]: Implementation in dominant strategies Revenue recovered at least OPT – O(n 2/3 ) Generic construction, many potential applications Ingredients: – Mechanism preserving differential privacy Making agents (almost) indifferent to what strategy they use Efficient (i.e., recovers most of OPT) – “Punishing” mechanism Making agents truthful Inefficient

7 What about you? Learn about differential privacy and mechanism design Extend known results – E.g., new applications Examine notions of privacy Explore relationships between privacy and game-theoretic concepts Using what? – Your knowledge in probability/statistics, algorithms, …, mathematics

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