The week's pick
Random Articles
Reseach Article
A Comparative study of Multi Agent Based and High-Performance Privacy Preserving Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 4 - Number 12 |
| Year of Publication: 2010 |
| Authors: Dr. Md Rizwan Beg, Md Muqeem, Md Faizan Farooqui |
10.5120/876-1247
|
Dr. Md Rizwan Beg, Md Muqeem, Md Faizan Farooqui . A Comparative study of Multi Agent Based and High-Performance Privacy Preserving Data Mining. International Journal of Computer Applications. 4, 12 ( August 2010), 23-26. DOI=10.5120/876-1247
Abstract
Data mining is an extraordinarily demanding field referring to extraction of implicit knowledge and relationships, which are not explicitly stored in databases. Agent paradigm presents a new way of conception and realizing of data mining system. The purpose is to combine different algorithms of data mining to prepare elements for decision-makers, benefiting from the possibilities offered by the multi-agent systems. While the emerging field of privacy preserving data mining (PPDM) will enable many new data mining applications, it suffers from several practical difficulties. PPDM algorithms are difficult to develop and computationally intensive to execute. Developers need convenient abstractions to reduce the costs of engineering PPDM applications. The individual parties involved in the data mining process need a way to bring high-performance, parallel computers to bear on the computationally intensive parts of the PPDM tasks. This paper discusses the comparative study between multi agent based data mining and high-performance privacy preserving data mining. This paper offers a detailed analysis of the agent framework for data mining and its overall architecture and functionality are presented and also challenges in developing PPDM algorithms with existing frameworks, and motivates the design of a new infrastructure based on these challenges.
References
- Fayyad U.M., Piatetsky-Shapiro G., Smyth P. (1996), « From Data Mining to KDD: an overview », AAAI/MIT Press, 1996.
- Han J. et Kamber M. (2002), Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, Canada, 2002.
- Ferber J. (1995), Les Systèmes multi-agents vers une intelligence collective, interEditions, France.
- A. Yao, “How to generate and exchange secrets,” in Proc. 27th Annual Symposium on Foundations of Computer Science, 1986, pp. 162–167.
- C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, “Tools for privacy preserving distributed data mining,” SIGKDD Explorations, vol. 4, no. 2, pp. 28–34, 2003.
- B. Pinkas, “Cryptographic techniques for privacy-preserving data mining,” SIGKDD Explor. Newsl., vol. 4, no. 2, pp. 12–19, 2002.
- M. Kantarcoglu and J. Vaidya, “Privacy preserving naive bayes classifier for horizontally partitioned data,” in IEEE ICDM Workshop on Privacy Preserving Data Mining, Melbourne, FL, November 2003, pp. 3–9.
- H. Yu, J. Vaidya, and X. Jiang, “Privacy-preserving svm classification on vertically partitioned data,” in Pan-Asia Conference on Knowledge Discover and Data Mining (PAKDD), Singapore, 2006, pp. 647–656.
- “Welcome to hadoop!” http://lucene.apache.org/hadoop/, 2007.
- B. Schneier, Applied Cryptography, 2nd ed. John Wiley & Sons, 1995.
- J. Secretan, M. Georgiopoulos, and J. Castro, “A privacy preserving probabilistic neural network for horizontally partitioned databases,”Aug. 2007.
- G. Jagannathan, K. Pillaipakkamnatt, and R. Wright, “A new privacypreserving distributed k-clustering algorithm,” in Proceedings of the 2006 SIAM International Conference on Data Mining (SDM), 2006.
- Z. Yang and R. N. Wright, “Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data,” in ICDEW ’05: Proceedings of the 21st International Conference on Data Engineering Workshops. Washington, DC, USA: IEEE Computer Society, 2005, p. 1196.
- G. Jagannathan and R. N. Wright, “Privacy-preserving distributed k-means clustering over arbitrarily partitioned data,” in KDD ’05: Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. New York, NY, USA: ACM Press, 2005, pp. 593–599.
- H. Yu, X. Jiang, and J. Vaidya, “Privacy-preserving svm using nonlinear kernels on horizontally partitioned data,” in Selected Areas in Cryptography, Dijon, France, 2006.
- Y. Lindell and B. Pinkas, “Privacy preserving data mining,” Journal of Cryptology, vol. 15, no. 3, 2002.
- M. Naor and B. Pinkas, “Oblivious transfer and polynomial evaluation,” in STOC ’99: Proceedings of the thirty-first annual ACM symposium on Theory of computing. New York, NY, USA: ACM Press, 1999, pp. 245–254.
- B. Goethals, S. Laur, H. Lipmaa, and T. Mielik¨ainen, “On private scalar product computation for privacy-preserving data mining.” In Information Security and Cryptology - ICISC 2004, 7th International Conference, Seoul, Korea, December 2-3, 2004, Revised Selected Papers, ser. Lecture Notes in Computer Science, C. Park and S. Chee, Eds., vol. 3506. Springer, 2004, pp. 104–120.
- H. Baazaoui Zghal, S. Faiz, and H. Ben Ghezala, “ A Framework for Data Mining Based Multi-Agent: An Application to Spatial Data” In World Academy of Science, Engineering and Technology 5 2005
- Jimmy Secretan, Anna Koufakou, Michael Georgiopoulos, “APHID: A Practical Architecture for High-Performance, Privacy-Preserving Data Mining”
Index Terms
Keywords