Synthesizing differentially private programs
Synthesizing differentially private programs
复制标题
综合差分私有程序
DOI：
10.1145/3341698
复制
发表时间：
2019
影响因子：
--
通讯作者：
Aws Albarghouthi
中科院分区：
文献类型：
--
作者：
Calvin Smith;Aws Albarghouthi
受数据分析任务激增的启发，最近的程序合成研究非常关注使用户能够通过直观的规范（如示例和自然语言）指定数据分析程序。然而，随着数据分析对隐私的威胁越来越大，我们认为有必要在正式隐私约束的情况下重新构想程序合成技术。在本文中，我们研究的问题，自动合成的随机化，差分私人程序，用户可以提供合成器所需的算法的隐私的约束。我们的技术基于一个线性依赖类型系统，可以跟踪程序消耗的资源，因此其隐私成本。我们开发了一种新的类型导向的合成算法，构造随机差分私有程序。我们应用我们的技术的问题，合成数据库查询以及递归差分隐私机制的文献。
Inspired by the proliferation of data-analysis tasks, recent research in program synthesis has had a strong focus on enabling users to specify data-analysis programs through intuitive specifications, like examples and natural language. However, with the ever-increasing threat to privacy through data analysis, we believe it is imperative to reimagine program synthesis technology in the presence of formal privacy constraints. In this paper, we study the problem of automatically synthesizing randomized, differentially private programs, where the user can provide the synthesizer with a constraint on the privacy of the desired algorithm. We base our technique on a linear dependent type system that can track the resources consumed by a program, and hence its privacy cost. We develop a novel type-directed synthesis algorithm that constructs randomized differentially private programs. We apply our technique to the problems of synthesizing database-like queries as well as recursive differential privacy mechanisms from the literature.
影响因子：
1.8
作者：
Albarghouthi, Aws;Hsu, Justin
通讯作者：
Hsu, Justin
影响因子：
--
作者：
Calvin Smith;Justin Hsu;Aws Albarghouthi
通讯作者：
Calvin Smith;Justin Hsu;Aws Albarghouthi