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Secure Multi Party Computation Technique for Classification Rule Sharing

by Murugeshwari B, Jayakumar C, Sarukesi K
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 7
Year of Publication: 2012
Authors: Murugeshwari B, Jayakumar C, Sarukesi K
10.5120/8764-2683

Murugeshwari B, Jayakumar C, Sarukesi K . Secure Multi Party Computation Technique for Classification Rule Sharing. International Journal of Computer Applications. 55, 7 ( October 2012), 1-10. DOI=10.5120/8764-2683

@article{ 10.5120/8764-2683,
author = { Murugeshwari B, Jayakumar C, Sarukesi K },
title = { Secure Multi Party Computation Technique for Classification Rule Sharing },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 7 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number7/8764-2683/ },
doi = { 10.5120/8764-2683 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:37.215553+05:30
%A Murugeshwari B
%A Jayakumar C
%A Sarukesi K
%T Secure Multi Party Computation Technique for Classification Rule Sharing
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 7
%P 1-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Confidentiality of data or resources is of primary importance in Privacy Preserving Data Mining (PPDM) Systems. The research work presented through this paper discusses the PPDM model in which the privacy of data transacted amongst the various Data Custodians involved is highlighted. The data available with each data custodian is assumed to be horizontally portioned. The proposed model considers the C5. 0 algorithm for data mining and classification rule generation due to its advances and classification accuracy over its predecessors. Privacy of the data transacted or secure multiparty computation is achieved by using the commutative RSA cryptography scheme. The proposed model is compared with the existing secure group communication techniques like Secure Lock and Asynchronous Control Polynomial in terms of computational efficiency. Furthermore the privacy preserving feature of the proposed scheme is proved in terms of the computational indistinguishablity of the data transacted amongst the varied data custodians involved discussed in the paper.

References
  1. H. Friedman and J. L. Bentley, "An Algorithm for Finding Best Matches in Logarithmic Expected Time," ACM Trans. Math. Software, vol. 3, no. 3, pp. 209-226, 1977.
  2. N. R. Adam and J. C. Wortmann, "Security-Control Methods for Statistical Databases: A Comparison Study," ACM Computing Surveys, vol. 21, no. 4, pp. 515-556, 1989.
  3. F Matthews, Gregory J. , Harel, Ofer," Data confidentiality: A review of methods for statistical disclosure limitation and methods for assessing privacy, Statistics Surveys, 5, (2011),pp 1-29 . DOI: 10. 1214/11-SS074.
  4. D. G. Marks, "Inference in MLS Database," IEEE Trans. Knowledge and Data Eng. , vol. 8, no. 1, pp. 46-55 Feb. 1996.
  5. D. E. O'Leary, "Knowledge Discovery as a Threat to Database Security," Proc. First Int'l Conf. Knowledge Discovery and Databases, pp. 107-516, 1991.
  6. M. -S. Chen, J. Han, and P. S. Yu, "Data mining: An overview from database perspective," IEEE Trans. Knowl. Data Eng. , 1996.
  7. C. Clifton and D. Marks, "Security and Privacy Implications of Data Mining," Proc. 1996 ACM Workshop Data Mining and Knowledge Discovery, 1996.
  8. C. Clifton et al. , "Tools for Privacy Preserving Distributed Data Mining," SIGKDD Explorations, vol. 4, no. 2, 2003, pp. 28-34.
  9. Nan Zhang and Wei Zhao,"Privacy-Preserving Data Mining Systems" IEEE Computer, Vol. 40, No. 4, Pp 5258, April 2007.
  10. V. S. Verykios et al. , "State-of-the-Art in Privacy Preserving Data Mining," SIGMOD Record, vol. 33, no. 1, 2004, pp. 50-57. 2001
  11. Y. Lindell and B. Pinkas, "Privacy preserving data mining," in Proc. Int'l Cryptology Conference (CRYPTO), 2000.
  12. R. Agrawal and R. Srikant, "Privacy preserving data mining," in Proc. ACM SIGMOD Int'l Conf. on Management of Data, 2000.
  13. Yaping Li, Minghua Chen, Qiwei Li and Wei Zhang,"Enabling Multi-level Trust in Privacy Preserving Data Mining" in IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2011. DOI 10. 1109/TKDE. 2011. 124.
  14. O. Goldreich, "Secure multi-party computation," Final (incomplete) draft, version 1. 4, 2002.
  15. A. W. -C. Fu, R. C. -W. Wong, and K. Wang, "Privacy-preserving frequent pattern mining across private databases," in Procedings of International Conference on Data Mining, 2005.
  16. Ming-Jun Xiao, Kai Han, Liu-Sheng Huang and Jing-Yuan Li,"Privacy Preserving C4. 5 Algorithm Over Horizontally Partitioned Data,"Proceedings of the Fifth IEEE International Conference on Grid and Cooperative Computing, 2006
  17. J. Vaidya and C. W. Clifton, "Privacy preserving association rule mining in vertically partitioned data," in Proc. ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 2002.
  18. Yanguang Shen, Hui Shao and Li Yang, "Privacy Preserving C4. 5 Algorithm over Vertically Distributed Datasets",2009 IEEE,International Conference on Networks Security, Wireless Communications and Trusted Computing,DOI 10. 1109/NSWCTC. 2009. 253, pp 446-448. 2009.
  19. J. Vaidya and C. Clifton, "Privacy-preserving k-means clustering over vertically partitioned data," in Proc. ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 2003.
  20. D. Agrawal and C. C. Aggarwal, "On the design and quantification of privacy preserving data mining algorithms," in Proceeding of the 20th ACM Symposium on Principles of Database Systems, Santa Barbara, California.
  21. K. Chen and L. Liu, "Privacy preserving data classification with rotation perturbation," in Proc. Int'l Conf. on Data Mining, 2005.
  22. S. Papadimitriou, F. Li, G. Kollios, and P. S. Yu, "Time series compressibility and privacy," in Proc. Int'l Conf. on Very Large Data Bases, 2007.
  23. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke, "Privacy preserving mining of association rules," in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
  24. W. Du and Z. Zhan, "Using randomized response techniques for privacy-preserving data mining," in Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003.
  25. R. Agrawal, R. Srikant, and D. Thomas, "Privacy preserving OLAP," in Proc. ACM SIGMOD Int'l Conf. on Management of Data, 2005.
  26. L. Sweeney, "k-anonymity: A model for protecting privacy," International Journal of Uncertainty, Fuzziness and Knowledge- Based Systems (IJUFKS), vol. 10, 2002.
  27. Tamir Tassa,"Secure Mining of Association Rules in Horizontally Distributed Databases". 2011
  28. L. Sweeney, "k-anonymity: A model for protecting privacy," International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (IJUFKS), vol. 10, 2002.
  29. C. C. Aggarwal and P. S. Yu, "A condensation approach to privacy preserving data mining," in Proc. Int'l Conf. on Extending Database Technology (EDBT), 2004.
  30. Slava Kisilevich, Lior Rokach, Yuval Elovici and Bracha Shapira,"Efficient Multidimensional Suppression for K-Anonymity",IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 3, MARCH 2010
  31. C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor. Our data, ourselves: Privacy via distributed noise generation. Advances in Cryptology-EUROCRYPT 2006, pages 486–503, 2006.
  32. M. Islam , L. Brankovic. Noise Addition for Protecting Privacy in Data Mining. Proceedings of The 6th Engineering Mathematics and Applications Conference (EMAC2003), Sydney, pages 85–90. Citeseer, 2003
  33. B. Pinkas," Cryptographic techniques for privacy-preserving data mining". ACM SIGKDD Explorations Newsletter, 4(2):19, 2002.
  34. G. H. Chiou and W. Chen, "Secure broadcasting using the Secure Lock," IEEE Transactions on Software Engineering, vol. 15, no. 8, pp. 929–934, Aug. 1989.
  35. X. Zou, Y. -S. Dai, and E. Bertino, "A practical and flexible key management mechanism for trusted collaborative computing," Proceedings of INFOCOM'08, Phoenix, AZ, USA, pp. 1211–1219, Apr. 2008
  36. Xukai Zou, Mingrui Qi, and Yan Sui. "A New Scheme for Anonymous Secure Group Communication",System Sciences (HICSS), 2011 44th Hawaii International Conference. 4-7 Jan. 2011.
  37. Lakshminath R. Dondeti , Sarit Mukherjee , Ashok Samal,"Survey and Comparison of Secure Group Communication Protocols ". CiteSeerX 1999 doi=10. 1. 1. 25. 7963.
  38. Christian Cachin and Jan Camenisch,IBM Research–Zurich,"Encrypting Keys Securely",IEEE COMPUTER AND RELIABILITY SOCIETIES. 1540-7993/10/. JULY/AUGUST 2010.
  39. Zhibin Zhou and Dijiang Huang,"An Optimal Key Distribution Scheme for Secure Multicast Group Communication",IEEE INFOCOM 2010.
  40. Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu and Philip S. Yu, et al. "Top 10 algorithms in data mining",Springer,Knowledge and Information Systems Volume 14, Number 1 (2008), 1-37, DOI: 10. 1007/s10115-007-0114-2
  41. Douglas Stinson,"Cryptography: Theory and Practice",CRC Press,ISBN: 0849385210
  42. Oded Goldreich,"Foundations of Cryptography Volume I . Basic Tools",Cambridge University Press, 2004. ISBN 978-0-511-54689-1 OCeISBN. ISBN 0-521-79172-3 hardback
  43. Oded Goldreich,"Foundations of Cryptography Volume II . Basic Applications",Cambridge University Press, 2004. ISBN 978-0-521-11991-7 paperback,ISBN 978-0-521-83084-3 hardback
  44. A. K. Lenstra, "Key length," Handbook of Information Security, Editorin-Chief, Hossein Bidgoli, vol. 2, pp. 617–635, 2005
  45. E. Bach and J. Shallit, "Algorithmic number theory, volume I: Efficient algorithms," The MIT Press, 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Privacy Preserving Data Mining Semi Honest Model Secure Multiparty Computation Commutative RSA C5. 0 Data mining Algorithm Classification Rules