A new permutation approach for distributed association rule mining

ABSTRACT

Privacy preserving distributed data mining has become a promising research area. This paper addresses the problem of association rule mining where the global database is vertically partitioned. When transactions are distributed in different sites, scalar product is a feasible tool to discover frequent itemsets. We present a new protocol to compute scalar product between two parties with a permutation approach. We analyze the protocol in detail and demonstrate its effectiveness and high privacy properties, and compare it to other published protocols.

REFERENCES

• 1: Jaideep Vaidya , Chris Clifton, Privacy preserving association rule mining in vertically partitioned data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775142]
• 2: Jaideep Vaidya , Chris Clifton, Leveraging the "Multi" in secure multi-party computation, Proceedings of the 2003 ACM workshop on Privacy in the electronic society, October 30, 2003, Washington, DC [doi>10.1145/1005140.1005149]
• 3: Wenliang Du , Zhijun Zhan, A practical approach to solve Secure Multi-party Computation problems, Proceedings of the 2002 workshop on New security paradigms, September 23-26, 2002, Virginia Beach, Virginia [doi>10.1145/844102.844125]
• 4: Jaideep Vaidya , Chris Clifton, Privacy-preserving k-means clustering over vertically partitioned data, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, August 24-27, 2003, Washington, D.C. [doi>10.1145/956750.956776]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems
          Subjects: Distributed databases
      H.2.7 Database Administration
          Subjects: Security, integrity, and protection

General Terms:
Algorithms, Management, Performance, Security

Keywords:
distributed data mining, privacy, scalar product, security