TY - GEN
T1 - Privapprox
T2 - 2017 USENIX Annual Technical Conference, USENIX ATC 2017
AU - Le Quoc, Do
AU - Beck, Martin
AU - Bhatotia, Pramod
AU - Chen, Ruichuan
AU - Fetzer, Christof
AU - Strufe, Thorsten
N1 - Publisher Copyright:
© USENIX Annual Technical Conference, USENIX ATC 2017. All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85077472099&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85077472099
T3 - Proceedings of the 2017 USENIX Annual Technical Conference, USENIX ATC 2017
SP - 659
EP - 672
BT - Proceedings of the 2017 USENIX Annual Technical Conference, USENIX ATC 2017
PB - USENIX Association
Y2 - 12 July 2017 through 14 July 2017
ER -