A Multi-Party Scheme for Privacy : Preserving Clustering

• Autorzy: Zhan J. , Matwin S. , Chang L.
• Języki publikacji: EN

Abstrakty

EN

Preserving data privacy while conducting data clustering among multiple parties is a demanding problem. We address this challenging problem in the following scenario: without disclosing their private data to each other, multiple parties, each having a private data set, want to collaboratively conduct k-medoids clustering. To tackle this problem, we develop secure protocols for multiple parties to achieve this dual goal. The solution is distributed, i.e., there is no central, trusted party having access to all the data. Instead, we define a protocol using homomorphic encryption and digital envelope techniques to exchange the data while keeping it private.

Słowa kluczowe

EN

privacy; security; clustering

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2006
• Tom: Vol. 1(7)
• Strony: 217--232
• Opis fizyczny: Bibliogr. 19 poz., tab.

Twórcy

autor

Zhan J.

• School of Information Technology and Engineering, University of Ottawa, Canada

autor

Matwin S.

• School of Information Technology and Engineering, University of Ottawa, Canada. Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland

autor

Chang L.

• Center for High Assurance Computer Systems, Naval Research Laboratory, USA

Bibliografia

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• 8. Vaidya J., and Clifton C. Privacy preserving association rule mining in vertically partitioned data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada.
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• 13. Merugu S., and Ghosh J. Privacy-preserving distributed clustering using generative models. In Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 24-27, 2003, Washington, DC, USA.
• 14. Oliveira S., and Zaiane O. Privacy preserving clustering by data transformation. In Proceedings of the 18th Brazilian Symposium on Databases, 304-318, Manaus, Brazil, October 6-8, 2003.
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• 16. Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In Advances in Cryptography - EUROCRYPT99, 223-238, Prague, Czech Republic, May 1999.
• 17. Rizvi S., and Haritsa J. Maintaining data privacy in association rule mining. In Proceedings of the 28th VLDB Conference, Hong Kong, China, 2002.
• 18. Sweeney L. k-anonymity: a model for protecting privacy. In International Journal on Uncertainty, fuzziness and Knowledge-based Systems, 10(5), 557-570, 2002.
• 19. Yao A. Protocols for secure computations. In Proceedings of the 23rd Annual IEEE Symposium on foundations of Computer Science, 1982.

Uwagi

PL

Błąd w numeracji bibliografii - podwójne numery 2 i 3.