Privacy Preserving Stream Analytics: The Marriage of Randomized Response and Approximate Computing
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.
Cite
@article{arxiv.1701.05403,
title = {Privacy Preserving Stream Analytics: The Marriage of Randomized Response and Approximate Computing},
author = {Do Le Quoc and Martin Beck and Pramod Bhatotia and Ruichuan Chen and Christof Fetzer and Thorsten Strufe},
journal= {arXiv preprint arXiv:1701.05403},
year = {2017}
}