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

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


Data mining, Privacy, Distributed processing


It is often required to conduct data mining among several sites to discover valuable patterns, associations, trends, and dependencies in the shared data. The privacy, however, is a concern. In many situations users do require that data min ing is conducted with no privacy of any site being leaked out to any other sites. In this paper a distributed privacy preserving data mining algorithm is proposed, which is characterized with its ability to resist the collusion, and es pecially, a system with more sites tends to have the abil ity to resist more collusion. Performance analysis results are provided for demonstrating the effectiveness of the pro posed algorithm.

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