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SMC Protocol for Naive Bayes Classification over Grid Partitioned Data using Multiple UTPs

by Alka Gangrade, Ravindra Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 6
Year of Publication: 2013
Authors: Alka Gangrade, Ravindra Patel
10.5120/10639-5392

Alka Gangrade, Ravindra Patel . SMC Protocol for Naive Bayes Classification over Grid Partitioned Data using Multiple UTPs. International Journal of Computer Applications. 64, 6 ( February 2013), 20-25. DOI=10.5120/10639-5392

@article{ 10.5120/10639-5392,
author = { Alka Gangrade, Ravindra Patel },
title = { SMC Protocol for Naive Bayes Classification over Grid Partitioned Data using Multiple UTPs },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 6 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number6/10639-5392/ },
doi = { 10.5120/10639-5392 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:15:41.951342+05:30
%A Alka Gangrade
%A Ravindra Patel
%T SMC Protocol for Naive Bayes Classification over Grid Partitioned Data using Multiple UTPs
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 6
%P 20-25
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The case where data is distributed horizontally as well as vertically, it refers as grid partitioned data. SMC protocol for Naïve Bayes classification over grid partitioned data is offered in this paper. Also present a solution of the Secure Multi-party Computation (SMC) problem in the form of a protocol that preserves privacy. In this system, a protocol with several Un-trusted Third Parties (UTPs) is used, where there is almost impossible of privacy leakage. Multiple UTPs will calculate the model parameters for integrating the horizontally partitioned data. After that secure multiplication protocol will apply on vertical partitioned data (multiple UTPs) to classify the new tuple. The main contribution of this paper is that it shows a simple and easy calculation for developing Naïve Bayes classifier for grid partitioned data. The evaluation method is simple and more efficient.

References
  1. Kuijpers, B. , Lemmens, V. and Moelans, B. 2008. Privacy Preserving ID3 over Horizontally, Vertically and Grid Partitioned Data, [cs. DB], 11 march 2008.
  2. Han, J. and Kamber, M. 2001. Data Mining: Concepts and Techniques, Elsevier, India.
  3. Mitchell, T. 1997. Machine Learning, 1st edn. McGraw-Hill Science/Engineering/Math, New York.
  4. Goldreich O. 1998. Secure multi-party computation, Sep 1998. (working draft).
  5. Kantarcioglu, M. and Vaidya, J. 2003. Privacy preserving naive Bayes classifier for horizontally partitioned data, In IEEE ICDM Workshop on Privacy Preserving Data Mining, Melbourne, FL, pp. 3-9, November 2003.
  6. Vaidya, J. and Clifton, C. 2004. Privacy preserving naive Bayes classifier on vertically partitioned data, Proc. SIAM International Conference on Data Mining, Lake Buena Vista, Florida, pp. 22-24, April 2004.
  7. Yang, Z. and Wright, R. 2006. Privacy-Preserving Computation of Bayesian Networks on Vertically Partitioned Data, IEEE Transactions on Data Knowledge Engineering, 18(9), April 2006, pp. 1253-1264.
  8. Yao, A. C. 1982. Protocols for secure computation, Proc. of 23rd IEEE Symposium on Foundations of Computer Science (FOCS), pp. 160-164.
  9. Du, W. and Attalah, M. J. 2001. Secure multi-problem computation problems and their applications: A review and open problems, Tech. Report CERIAS Tech Report 2001-51, Center for Education and Research in Information Assurance and Security and Department of Computer Sciences, Purdue University, West Lafayette, IN 47906.
  10. Verykios, V. and Bertino, E. 2004. State-of-the-art in Privacy preserving Data Mining, SIGMOD Record, 33(1), pp. 50-57.
  11. Cliffton, C. , Kantarcioglu, M. and Vaidya, J. 2004. Tools for privacy preserving distributed data mining, ACM SIGKDD Explorations Newsletter, 4(2), pp. 28-34.
  12. Agrawal, R. and Srikant, R. 2000. Privacy preserving data mining, Proc. of the ACM SIGMOD on Management of data, Dallas, TX USA, pp. 439-450, May 15-18.
  13. Agrawal, D. and Aggarwal, C. C. 2001. On the design and quantification of privacy preserving data mining algorithms, Proc. of the Twentieth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Santa Barbara, California, USA, pp. 247–255, May 21-23 2001.
  14. Quinlan, J. R. 1990. Induction of decision trees, in: Jude W. Shavlik, Thomas G. Dietterich, (Eds. ), Readings in Machine Learning, Morgan Kaufmann, 1, pp. 81–106.
  15. Lindell, Y. and Pinkas, B. 2002. Privacy preserving data mining, Journal of Cryptology, 15(3), pp. 177–206.
  16. Emekci, F. , Sahin, O. D. , Agrawal, D. and Abbadi, A. E. 2007. Privacy preserving decision tree learning over multiple parties, Data & Knowledge Engineering 63, pp. 348-361.
  17. Shamir, A. 1979. How to share a secret, Communications of the ACM, 22(11), pp. 612-613.
  18. Du, W. and Zhan, Z. 2002. Building decision tree classifier on private data, In CRPITS, pp. 1–8.
  19. Fang, W. and Yang, B. 2008. Privacy Preserving Decision Tree Learning Over Vertically Partitioned Data, Proc. of the 2008 International Conference on Computer Science & Software Engineering, pp. 1049-1052.
  20. Vaidya, J. 2004. Privacy preserving data mining over vertically partitioned data, doctoral diss. , Purdue University, August 2004.
  21. Vaidya, J. , Clifton, C. , Kantarcioglu, M. and Patterson, A. S. 2008. Privacy-preserving decision trees over vertically partitioned data, Proc. of the 19th Annual IFIP WG 11. 3 Working Conference on Data and Applications Security, pp. 139–152.
  22. Gangrade, A. and Patel, R. 2011. A novel protocol for privacy preserving decision tree over horizontally partitioned data, International Journal of Advanced Research in Computer Science, 2 (1), pp. 305-309.
  23. Gangrade, A. and Patel, R. 2012. Privacy preserving two-layer decision tree classifier for multiparty databases, International Journal of Computer and Information Technology (2277 – 0764), 1(1), pp. 77-82.
  24. Gangrade, A. and Patel, R. 2012. Privacy preserving Naïve Bayes classifier for horizontally distribution scenario using Un-trusted Third Party, IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727, Volume 7, Issue 6 (Nov. - Dec. 2012), pp 04-12.
  25. Gangrade, A. and Patel, R. 2012. Privacy preserving three-layer Naïve Bayes classifier for vertically partitioned databases, paper communicated in Journal of Information and computing Science (JIC), ISSN 1746-7659 (print).
  26. Vaidya, J. , Kantarcioglu, M. and Clifton, C. 2008. Privacy-preserving Naïve Bayes classification. The VLDB Journal (2008) 17, pp. 879–898.
  27. Witten, I. H. , Frank, E. and Hall, M. A. 2011. Data Mining Practical Machine Learning Tools and Techniques, Burlington, MA, Morgan Kaufmann.
Index Terms

Computer Science
Information Sciences

Keywords

Privacy preserving probability secure multiplication protocol grid partitioned UTP

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