Privacy-Preserving Data Analytics

Do Le Quoc, Martin Beck, Pramod Bhatotia, Ruichuan Chen, Christof Fetzer, Thorsten Strufe

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract / Description of output

Real-time processing of user data streams in online services inadvertently creates tension between the users and analysts: users are looking for stronger privacy, while analysts desire for higher utility data analytics in real time. To resolve this tension, this paper describes the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing.

PRIVAPPROX provides three important properties: (i) Privacy: zero-knowledge privacy guarantee for users, a privacy bound tighter than the state-of-the-art differential privacy; (ii) Utility: an interface for data analysts to systematically explore the trade-offs between the output accuracy (with error estimation) and the query execution budget; (iii) Latency: near real-time stream processing based on a scalable “synchronization-free” distributed architecture. The key idea behind PRIVAPPROX is to combine two techniques together, namely, sampling (used for approximate computation) and randomized response (used for privacy-preserving analytics). The resulting combination is complementary—it achieves stronger privacy guarantees, and also improves the performance for stream analytics.

Original languageEnglish
Title of host publicationEncyclopedia of Big Data Technologies
EditorsSherif Sakr, Albert Zomaya
Number of pages9
ISBN (Electronic)978-3-319-77525-8
ISBN (Print)3319775243, 978-3319775241, 978-3-319-77526-5
Publication statusPublished - 1 Mar 2019


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