Differentially-private software analytics for mobile apps: opportunities and challenges

Abstract

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.