LightDP: towards automating differential privacy proofs

Danfeng Zhang, Daniel Kifer
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引用次数: 62

Abstract

The growing popularity and adoption of differential privacy in academic and industrial settings has resulted in the development of increasingly sophisticated algorithms for releasing information while preserving privacy. Accompanying this phenomenon is the natural rise in the development and publication of incorrect algorithms, thus demonstrating the necessity of formal verification tools. However, existing formal methods for differential privacy face a dilemma: methods based on customized logics can verify sophisticated algorithms but come with a steep learning curve and significant annotation burden on the programmers, while existing programming platforms lack expressive power for some sophisticated algorithms. In this paper, we present LightDP, a simple imperative language that strikes a better balance between expressive power and usability. The core of LightDP is a novel relational type system that separates relational reasoning from privacy budget calculations. With dependent types, the type system is powerful enough to verify sophisticated algorithms where the composition theorem falls short. In addition, the inference engine of LightDP infers most of the proof details, and even searches for the proof with minimal privacy cost when multiple proofs exist. We show that LightDP verifies sophisticated algorithms with little manual effort.
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LightDP:实现差异化隐私证明的自动化
在学术和工业环境中,差异隐私的日益普及和采用导致了在保护隐私的同时发布信息的越来越复杂的算法的发展。伴随这一现象的是不正确算法的开发和发表的自然增加,从而证明了形式化验证工具的必要性。然而,现有的差分隐私的形式化方法面临着一个困境:基于自定义逻辑的方法可以验证复杂的算法,但会带来陡峭的学习曲线和程序员的重大注释负担,而现有的编程平台缺乏对某些复杂算法的表达能力。在本文中,我们介绍了LightDP,一种简单的命令式语言,它在表达能力和可用性之间取得了更好的平衡。LightDP的核心是一个新的关系类型系统,它将关系推理与隐私预算计算分离开来。有了依赖类型,类型系统就足够强大,可以在组合定理不足的地方验证复杂的算法。此外,LightDP的推理引擎可以推断出大部分的证明细节,甚至在存在多个证明的情况下搜索隐私成本最小的证明。我们展示了LightDP用很少的人工努力验证复杂的算法。
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