Privacy-Preserving Two-Party Distributed Association Rules Mining on Horizontally Partitioned Data

In many applications, data mining has to be done in distributed data scenarios. In such situations, data owners may be concerned with the misuse of data, hence, they do not want their data to be mined, especially when these contain sensitive information. Privacy-preserving Data Mining (PPDM) aims to protect data privacy in the course of data mining. Privacy preserving distributed association rules mining protocols have been developed for horizontally partitioned data scenarios with more than two participating parties. However, they depend on a secure multi-party summary and union computation, which cannot guarantee security while the number of participating parties is two. We use commutative encryption and design a secure division computation protocol as the core techniques to implement the protocols for the privacy-preserving two-party distributed mining of association rule mining. The protocols' security and performance are analyzed.