In this paper, we present a push-button, automated technique for verifying $\epsilon$-differential privacy of sophisticated randomized algorithms. We make a range of conceptual, algorithmic,and practical contributions: (1) Inspired by the recent advances on approximate couplings and randomness alignment, we present a new proof technique called coupling strategies, which casts differential privacy proofs as discovering a strategy in a game where we have finite privacy resources to expend. (2) To discover a winning strategy, we present a constraint-based formulation of the problem as solving a set of Horn modulo couplings (HMC) constraints, a novel kind of constraints combining first-order Horn clauses with probabilistic constraints. (3) We present a technique for solving HMC constraints by transforming probabilistic constraints into logical constraints with uninterpreted functions. (4) Finally, we implement our technique and provide the first automated proofs of a number of algorithms from the differential privacy literature, including Report Noisy Max, the Exponential Mechanism, and the Sparse Vector Mechanism.
Fri 12 Jan (GMT-07:00) Tijuana, Baja California change
|13:40 - 14:05|
|14:05 - 14:30|
|14:30 - 14:55|
|14:55 - 15:20|
Adam ŚcibiorUniversity of Cambridge and MPI Tuebingen, Ohad KammarUniversity of Oxford, Matthijs VákárUniversity of Oxford, Sam StatonUniversity of Oxford, Hongseok YangUniversity of Oxford, Yufei CaiUniversity of Tuebingen, Klaus OstermannUniversity of Tuebingen, Sean K. MossUniversity of Cambridge, Chris HeunenUniversity of Edinburgh, Zoubin GhahramaniUniversity of Cambridge