Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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
Rocznik
Tom
Strony
217--232
Opis fizyczny
Bibliogr. 19 poz., tab.
Twórcy
autor
- School of Information Technology and Engineering, University of Ottawa, Canada
autor
- School of Information Technology and Engineering, University of Ottawa, Canada. Institute for Computer Science, Polish Academy of Sciences, Warsaw, Poland
autor
- Center for High Assurance Computer Systems, Naval Research Laboratory, USA
Bibliografia
- 1. Aggarwal G., Mishra N., and Pinkas B. Secure computation of the kth-rankerd element. In EUROCRYPT, 40-55, 2005.
- 2. Agrawal R. and Srikant R. Privacy-preserving data mining. In Proceedings of the ACM SIGMOD Conference on Management of Data, 439-450, ACM Press, May 2000.
- 3. Gehrke J. E., Evfimievshi A., and Srikant R. Limiting privacy breaches in privacy preserving data mining. In Proceedings of the 22nd ACM SIGMOD Symposium on Principles of Database Systems, San Diego, CA, June 2003.
- 2. Berkhin P. Survey of clustering data mining techniques. Technical Report, Accrue Software, San Jose, CA, 2002.
- 3. Domingo-Ferrer J. A provably secure additive and multiplicative privacy homomorphism. In Information Security Conference, 471-483, 2002.
- 4. Du W., and Zhan Z. Using randomized response techniques for privacy- preserving data mining. In Proceedings of The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24-27, 2003.
- 5. Dwork C., and Nissim K. Privacy-preserving data mining on vertically partitioned databases. In CRYPTO 2004, 528-544.
- 6. Goethals B, Laur S., Lipmaa H., and Mielikainen T. On secure scalar product computation for privacy-preserving data mining. In Proceedings of The 7th Annual International Conference of Information Security and Cryptology, volume 3506 of Lecture Notes in Computer Science, 104-120, Seoul, Korea, December 2-3, 2004, Springer-Verlag.
- 7. Goldreich O. Secure multi-party computation (working draft). http://www.wisdom,weizmann.ac.il/home/oded/public_html/foc.html, 1998.
- 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.
- 9. Vaidya J., and Clifton C. Privacy preserving k-means clustering over vertically partitioned data. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, Washington DC, USA.
- 10. Kaufman L., and Rousseeuw. Finding groups in data. Wiley, New York, NY, 1990.
- 11. Klusch M., Lodi S., and Moro G-L. Distributed clustering based on sampling local density estimates. In Proceedings of International Joint Conference on Artificial Intelligence, Mexico, 2003.
- 12. Lindell Y., and Pinkas B. Privacy preserving data mining. In Advances in Cryptology - Crypto2000, Lecture Notes in Computer Science, volume 1880, 2000.
- 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.
- 15. Oliveira S., and Zaiane O. Privacy preserving clustering by data object similarity-based representation and dimensionality reduction transformation. In Workshop on Privacy and Security Aspects of Data Mining in conjuction with the 4th IEEE International Conference on Data Mining, 21-30, Brighton, UK, November 1, 2004.
- 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6dacd9e1-1532-4d3b-aba8-c036a451014c