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...

• Type: Journal
• Year: 2010
• Where: USS
• Authors: Yitao Duan, NetEase Youdao, John Canny, Justin Z. Zhan
• Keywords: Algorithm, Large Scale, Operating System, Privacy, USS 2010