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...

• Type: Conference
• Year: 2008
• Where: SDM
• Authors: Yitao Duan, John F. Canny
• Keywords: Algorithms, Data Mining, Non-linear Algorithms, SDM 2008