SHF: Small: PrivAid: Differentially-Private Analytics for Android Apps

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Mobile apps often use analytics infrastructures to gather data on appperformance and user behaviors. There are significant privacy concernsabout the collection and use of such data. Legislative efforts andsocietal demands are calling for increased transparency andwell-defined compromises between the utility of data gathering and thecorresponding loss of privacy. This project will improve the privacyof mobile app analytics by employing differential privacy, whichprovides a rigorous theory and a powerful algorithmic framework forprivacy-preserving analysis. Integration of researchand education will develop the expertise of developers of mobile appsand data analyses. Recruitment of underrepresented students willcontribute to increased diversity in computing. The project willadvance the important area of user privacy, in an environment withrapidly-increasing societal and legislative demands for privacy.The investigators will develop rigorous and quantifiable privacymechanisms in mobile-app analytics by leveraging the theory of localdifferential privacy (LDP). The first contribution of the project willbe the design and implementation of PrivAid, a conceptual approach andrelated software tools for deploying LDP analyses in widely-usedinfrastructures for mobile-app analytics. Based on this foundation,several algorithms targeting important software analyses will bedeveloped using novel techniques for randomization and sampling: (1)edge/path profiling in control-flow graphs; (2) analyses of heavyhitters for event profiling and graph profiling; (3) profiling undercontinuous observation. The public release of PrivAid, softwareanalyses, and experimental subjects will enable the development andevaluation of future LDP software analyses.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.