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Published Articles >> Table of Contents >> Abstract
Fourth IEEE International Conference on Data Mining (ICDM'04)
pp. 487-490
Privacy-Sensitive Bayesian Network Parameter Learning
D. Meng, Washington State University, Pullman, WA
K. Sivakumar, Washington State University, Pullman, WA
H. Kargupta, UMBC, Baltimore, MD
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10076
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| Abstract |
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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).
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Citation:
D. Meng, K. Sivakumar, H. Kargupta,
"Privacy-Sensitive Bayesian Network Parameter Learning,"
icdm,
pp. 487-490,
Fourth IEEE International Conference on Data Mining (ICDM'04),
2004
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