A High Collusion-Resistant Approach to Distributed Privacy-preserving Data Mining

抄録

Data mining across different companies, organizations, online shops, or the likes, called sites, is necessary so as to discover valuable shared patterns, associations, trends, or dependencies in their shared data. Privacy, however, is a concern. In many situations it is required that data mining should be conducted without any privacy being violated. In response to this requirement, this paper proposes an effective distributed privacy-preserving data mining approach called CRDM (Collusion-Resistant Data Mining). CRDM is characterized by its ability to resist the collusion. Unless all sites but the victim collude, privacy of a site cannot be violated. Considering that for such applications that need not so high a level of security, excess security assurance would incur extra costs in communication, an extension scheme is also presented so that communication cost can be restrained while still maintaining a user-specified level of security. Results of both analytical and experimental performance study demonstrate the effectiveness of CRDM.