Privapprox: Privacy-preserving stream analytics

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

30 Scopus citations


How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe 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 our approach is to marry two techniques together, namely, sampling (used for approximate computation) and randomized response (used for privacy-preserving analytics). The resulting marriage is complementary - it achieves stronger privacy guarantees, and also improves the performance for stream analytics.

Original languageEnglish
Title of host publicationProceedings of the 2017 USENIX Annual Technical Conference, USENIX ATC 2017
PublisherUSENIX Association
Number of pages14
ISBN (Electronic)9781931971386
StatePublished - 2019
Externally publishedYes
Event2017 USENIX Annual Technical Conference, USENIX ATC 2017 - Santa Clara, United States
Duration: 12 Jul 201714 Jul 2017

Publication series

NameProceedings of the 2017 USENIX Annual Technical Conference, USENIX ATC 2017


Conference2017 USENIX Annual Technical Conference, USENIX ATC 2017
Country/TerritoryUnited States
CitySanta Clara


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