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

S. Urabe, J. Wang, E. Kodama, and T. Takata (Japan)


Data mining, preserving privacy, distributed processing, collusion-resistance.


Data mining across different companies, organizations, online shops, or the likes is necessary so as to discover valuable shared patterns, associations, trends, or depen dencies 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. Let the number of sites participating in data mining be M. Unless the number of colluding sites is not less than M − 1, privacy cannot be violated. Results of both analytical and experimental performance study demonstrated the effectiveness of CRDM.

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