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Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining

9 years 22 days ago
Practical Private Computation and Zero-Knowledge Tools for Privacy-Preserving Distributed Data Mining
In this paper we explore private computation built on vector addition and its applications in privacypreserving data mining. Vector addition is a surprisingly general tool for implementing many algorithms prevalent in distributed data mining. Examples include linear algorithms like voting and summation, as well as non-linear algorithms such as SVD, PCA, k-means, ID3, machine learning algorithms based on Expectation Maximization (EM), etc., and all algorithms in the statistical query model [27]. The non-linear algorithms aggregate data only in certain steps, such as conjugate gradient, which are linear in the data. We introduce a new and highly efficient VSS (Verifiable Secret-Sharing) protocol in a special but widely-applicable model that allows secret-shared arithmetic operations in such aggregation steps to be done over small fields (e.g. 32 or 64 bits). There are two major advantages: (1) in this framework private arithmetic operations have the same cost as normal arithmetic and (2...
Yitao Duan, John F. Canny
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where SDM
Authors Yitao Duan, John F. Canny
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