Privacy-Sensitive Bayesian Network Parameter Learning

Authors:
D. Meng;K. Sivakumar;H. Kargupta
Affiliations:
Washington State University, Pullman, WA;Washington State University, Pullman, WA;UMBC, Baltimore, MD
Venue:
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Year:
2004

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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 relevantdifferent 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).