Name
Title | ||
|---|---|---|
Privacy Preserving Stream Analytics: The Marriage of Randomized Response and Approximate Computing. |
Abstract | ||
|---|---|---|
How to preserve usersu0027 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. |
Year | Venue | Field |
|---|---|---|
2017 | arXiv: Distributed, Parallel, and Cluster Computing | Data analysis,Differential privacy,Computer science,Sampling (statistics),Randomized response,Stream processing,Analytics,Database,Privacy software,Scalability,Distributed computing |
DocType | Volume | Citations |
Journal | abs/1701.05403 | 3 |
PageRank | References | Authors |
0.38 | 46 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Le Quoc, D. | 1 | 55 | 8.21 |
| Martin Beck | 2 | 16 | 4.14 |
| Pramod Bhatotia | 3 | 414 | 28.94 |
| Ruichuan Chen | 4 | 205 | 18.95 |
| Christof Fetzer | 5 | 2429 | 172.89 |
| Thorsten Strufe | 6 | 846 | 80.61 |