document: Privacy-preserving Bayesian network structure computation on distributed heterogeneous data As more and more activities are carried out using computers and computer networks , the amount of potentially sensitive data stored by business , governments , and other parties increases . Different parties may wish to benefit from cooperative use of their data , but privacy regulations and other privacy concerns may prevent the parties from sharing their data . Privacy-preserving data mining provides a solution by creating distributed data mining algorithms in which the underlying data is not revealed . In this paper , we present a privacy-preserving protocol for a particular data mining task : learning the Bayesian network structure for distributed heterogeneous data . In this setting , two parties owning confidential databases wish to learn the structure of Bayesian network on the combination of their databases without revealing anything about their data to each other . We give an efficient and privacy-preserving version of the K2 algorithm to construct the structure of a Bayesian network for the parties ' joint data .
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