Sciweavers

Share
USS
2010

P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users

7 years 9 months ago
P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users
In this paper we introduce a framework for privacypreserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy (P4P) and features a novel heterogeneous architecture and a number of efficient tools for performing private computation and ensuring security at large scale. It maintains the following properties: (1) Provably strong privacy; (2) Adequate efficiency at reasonably large scale; and (3) Robustness against realistic adversaries. The framework gains its practicality by decomposing data mining algorithms into a sequence of vector addition steps that can be privately evaluated using a new verifiable secret sharing (VSS) scheme over small field (e.g., 32 or 64 bits), which has the same cost as regular, non-private arithmetic. This paradigm supports a large number of statistical learning algorithms including SVD, PCA, k-means, ID3, EM-based machine learning algorithms, etc., and all algorithms in the statistical quer...
Yitao Duan, NetEase Youdao, John Canny, Justin Z.
Added 15 Feb 2011
Updated 15 Feb 2011
Type Journal
Year 2010
Where USS
Authors Yitao Duan, NetEase Youdao, John Canny, Justin Z. Zhan
Comments (0)
books