Privacy-Preserving Bayesian Network Learning From Heterogeneous Distributed Data

In this paper, we propose a post randomization technique to learn a Bayesian network (BN) from distributed heterogeneous data, in a privacy sensitive fashion. In this case, two or more parties own sensitive data but want to learn a Bayesian network from the combined data. We consider both structure and parameter learning for the BN. The only required information from the data set is a set of sufficient statistics for learning both network structure and parameters. The proposed method estimates the sufficient statistics from the randomized data. The estimated sufficient statistics are then used to learn a BN. For structure learning, we face the familiar extra-link problem since estimation errors tend to break the conditional independence among the variables. We propose modifications of score functions used for BN learning, to solve this problem. We show both theoretically and experimentally that post randomization is an efficient, flexible, and easy-to-use method to learn Bayesian network from privacy sensitive data.