A high collusion-resistant approach to distributed privacy-preserving data mining

Authors:
Shintaro Urabe;Jiahong Wong;Eiichiro Kodama;Toyoo Takata
Affiliations:
Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan;Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan;Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan;Faculty of Software and Information Science, Iwate Prefectural University, Takizawa, Iwate, Japan
Venue:
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
Year:
2007

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Abstract

Data mining across different companies, organizations, online shops, or the likes 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. 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.