Synthesizing Coupling Proofs of Differential Privacy
Synthesizing Coupling Proofs of Differential Privacy
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DOI：
10.1145/3158146
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发表时间：
2018-01-01
影响因子：
1.8
通讯作者：
Hsu, Justin
中科院分区：
文献类型：
--
作者：
Albarghouthi, Aws;Hsu, Justin
差异隐私已成为一种有希望的隐私概率表达，在学术界和行业中产生了强烈的兴趣。我们提出了一种按钮，自动化的技术，用于验证复杂的随机算法的C-差异隐私。我们做出了几种概念性，算法和实践贡献：（i）受到最近在近似耦合和随机性对齐方式的进步的启发，我们提出了一种称为耦合策略的新证明技术，该技术将差异隐私证明作为我们在游戏中的胜利策略中，将拥有有限的隐私资源来支出。 （ii）要发现获胜策略，我们提出了对问题的基于约束的限制，作为一组Horn模量耦合（RTMC）约束，这是一阶Horn条款和概率约束的新型组合。 （iii）我们提出了一种通过将概率约束转换为具有未解释功能的逻辑约束来解决石灰约束的技术。 （iv）最后，我们在Fairsquare验证器中实施了我们的技术，并为来自差异隐私文献的许多具有挑战性的算法提供了第一个自动隐私证明，包括报告噪声Max，指数机制和稀疏矢量机制。
Differential privacy has emerged as a promising probabilistic formulation of privacy, generating intense interest within academia and industry. We present a push-button, automated technique for verifying c-differential privacy of sophisticated randomized algorithms. We make several conceptual, algorithmic, and practical contributions: (i) 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 a winning strategy in a game where we have finite privacy resources to expend. (ii) To discover a winning strategy, we present a constraint-based fommlation of the problem as a set of Horn modulo couplings (rtmc) constraints, a novel combination of first-order Horn clauses and probabilistic constraints. (iii) We present a technique for solving lime constraints by transforming probabilistic constraints into logical constraints with uninterpreted functions. (iv) Finally, we implement our technique in the FairSquare verifier and provide the first automated privacy proofs for a number of challenging algorithms from the differential privacy literature, including Report Noisy Max, the Exponential Mechanism, and the Sparse Vector Mechanism.