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Privacy Preserving Stream Analytics: The Marriage of Randomized Response and Approximate Computing

D. Quoc,Martin Beck,Pramod Bhatotia,Ruichuan Chen,C. Fetzer,T. Strufe

Published 2017 in arXiv.org

ABSTRACT

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 properties: (i) Privacy: zero-knowledge privacy guarantees 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 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 existing techniques together: namely, sampling (used in the context of approximate computing) and randomized response (used in the context of privacy-preserving analytics). The resulting marriage is complementary - it achieves stronger privacy guarantees and also improves performance, a necessary ingredient for achieving low-latency stream analytics.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    arXiv.org

  • Publication date

    2017-01-19

  • Fields of study

    Computer Science

  • Identifiers
    arXiv 1701.05403
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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