Differentially-Private Software Analytics for Mobile Apps: Opportunities and Challenges

被引：1

作者：

Zhang, Hailong ^{[1
]}

Latif, Sufian ^{[1
]}

Bassily, Raef ^{[1
]}

Rountev, Atanas ^{[1
]}

机构：

[1] Ohio State Univ, Columbus, OH 43210 USA

来源：

PROCEEDINGS OF THE 4TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON SOFTWARE ANALYTICS (SWAN'18) | 2018年

关键词：

software analytics; differential privacy; mobile apps;

D O I：

10.1145/3278142.3278148

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Software analytics libraries are widely used in mobile applications, which raises many questions about trade-offs between privacy, utility, and practicality. A promising approach to address these questions is differential privacy. This algorithmic framework has emerged in the last decade as the foundation for numerous algorithms with strong privacy guarantees, and has recently been adopted by several projects in industry and government. This paper discusses the benefits and challenges of employing differential privacy in software analytics used in mobile apps. We aim to outline an initial research agenda that serves as the starting point for further discussions in the software engineering research community.

引用

页码：26 / 29

页数：4

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