Fourth IEEE International Conference on Data Mining (ICDM'04) (2004)
Brighton, United Kingdom
Nov. 1, 2004 to Nov. 4, 2004
D. Meng , Washington State University, Pullman, WA
K. Sivakumar , Washington State University, Pullman, WA
H. Kargupta , UMBC, Baltimore, MD
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).
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.