PrivApprox: Privacy-Preserving Stream Analytics


Do Le Quoc and Martin Beck, TU Dresden; Pramod Bhatotia, The University of Edinburgh; Ruichuan Chen, Nokia Bell Labs; Christof Fetzer and Thorsten Strufe, TU Dresden


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.

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@inproceedings {203237,
author = {Do Le Quoc and Martin Beck and Pramod Bhatotia and Ruichuan Chen and Christof Fetzer and Thorsten Strufe},
title = {PrivApprox: Privacy-Preserving Stream Analytics},
booktitle = {2017 {USENIX} Annual Technical Conference ({USENIX} {ATC} 17)},
year = {2017},
isbn = {978-1-931971-38-6},
address = {Santa Clara, CA},
pages = {659--672},
url = {},
publisher = {{USENIX} Association},