Mining Frequent Patterns Securely in Distributed System*A part of this paper was presented at the international conference on intelligent data engineering and automated learning, UK., 2004. This work was supported in part by the Japanese Society for the Promotion of Science (JSPS).

  • Authors:

  • Jiahong Wang;Takuya Fukasawa;Shintaro Urabe;Toyoo Takata;Masatoshi Miyazaki

  • Affiliations:

  • The authors are with Iwate Prefectural University, Iwate-ken, 020--0193 Japan. E-mail: wjh@iwate-pu.ac.jp,;The author is with Project-EF Corporation, Tokyo, 103--0014 Japan.,;The authors are with Iwate Prefectural University, Iwate-ken, 020--0193 Japan. E-mail: wjh@iwate-pu.ac.jp,;The authors are with Iwate Prefectural University, Iwate-ken, 020--0193 Japan. E-mail: wjh@iwate-pu.ac.jp,;The author is with Argo Solutions Co., Ltd., Sendai-shi, 980--0811 Japan.

  • Venue:
  • IEICE - Transactions on Information and Systems
  • Year:
  • 2006

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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, in this paper we propose an effective distributed privacy-preserving data mining approach called SDDM. SDDM is characterized by its ability to resist collusion. Unless the number of colluding sites in a distributed system is larger than or equal to 4, privacy cannot be violated. Results of performance study demonstrated the effectiveness of SDDM.