Fourth IEEE International Conference on Data Mining (ICDM'04) (2004)

Brighton, United Kingdom

Nov. 1, 2004 to Nov. 4, 2004

ISBN: 0-7695-2142-8

pp: 487-490

D. Meng , Washington State University, Pullman, WA

K. Sivakumar , Washington State University, Pullman, WA

H. Kargupta , UMBC, Baltimore, MD

ABSTRACT

This paper considers the problem of learning the parameters of a Bayesian Network, assuming the structure of the network is given, from a privacy-sensitive dataset that is distributed between multiple parties. For a binary-valued dataset, we show that the count information required to estimate the conditional probabilities in a Bayesian network can be obtained as a solution to a set of linear equations involving some inner product between the relevant different feature vectors. We consider a random projection-based method that was proposed elsewhere to securely compute the inner product (with a modified implementation of that method).

INDEX TERMS

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CITATION

D. Meng, H. Kargupta and K. Sivakumar, "Privacy-Sensitive Bayesian Network Parameter Learning,"

*Fourth IEEE International Conference on Data Mining (ICDM'04)(ICDM)*, Brighton, United Kingdom, 2004, pp. 487-490.

doi:10.1109/ICDM.2004.10076

CITATIONS