Privacy Preserving Distributed Data Mining using Randomized Site Selection

European Journal of Scientific Research

Volumn 64, Issue 4, 2011, Pages 610-624

  Rajalakshmi, M ^a   Purusothaman, T ^b  

^a Department of Artificial Intelligence and Machine Learning   (India)

^b GOVERNMENT COLLEGE OF TECHNOLOGY   (India)

Author keywords

Distributed data mining; Elliptic Curve Cryptography (ECC); Frequent itemsets; Privacy; Sanitization; Secure multiparty computation

Indexed keywords

EID: 81855183816     PISSN: 1450216X     EISSN: 1450202X     Source Type: Journal    
DOI: None     Document Type: Article

References (29)

1. Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules. In Proceedings of the 20th VLDB Conference Santiago, Chile, 487-499.
2. Ashrafi, M., Taniar, D. & Smith, K. (2004). ODAM : An Optimized Distributed Association Rule Mining Algorithm, IEEE Distributed Systems Online, 5(3).
3. Ashrafi, M., Taniar, D. & Smith, K. (2005). Privacy-Preserving Distributed Association Rule Mining Algorithm. International Journal of Intelligent Information Technologies, 1(1), 46-69.
4. Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M. & Verykios, V.S. (1999). Disclosure limitation of sensitive rules. In Proceedings of the IEEE Knowledge and Data Exchange Workshop (KDEX'99). IEEE Computer Society, 45-52.
5. Burnett.A, Winters.K, and Dowling.T, (2002). A Java implementation of an elliptic curve Cryptosystem-Java programming and practices. In Proceedings of the inaugural conference on the Principles and Practice of programming.
6. Cheung, D., Ng, V., Fu, A. & Fu, Y.(1996). Efficient Mining of Association Rules in Distributed Databases. IEEE Transactions on Knowledge and Data Engineering. 8(6), 911-922. (Pubitemid 126774146)
7. Clifton, C. (2001) Secure Multiparty Computation Problems and Their Applications: A Review and Open Problems. In Proceedings of the Workshop on New Security Paradigms, Cloudcroft, New Mexico,.
8. Clifton, C., Kantarcioglu, M. & Vaidya, J.(2004). Defining privacy for data mining. Book Chapter Data Mining, Next generation challenges and future directions.
9. Goldreich, O., Micali, S. & Wigderson. (1987). How to Play any Mental Game - A Completeness Theorem For Protocols With Honest Majority. In 19th ACM symposium on the theory of computing. 218-229. (Pubitemid 18643409)
10. Goldreich, O. (1998). Secure Multiparty Computation (Working Draft).
11. Han, J. & Kamber, M. (2001). Data Mining: Concepts and Technique. Morgan Kaufmann Publishers.
12. Han, J. Pei, J. , Yin,Y. & Mao,R.(2004). Mining Frequent Patterns without Candidate Generation : A Frequent Pattern Approach. IEEE Transactions on Data Mining and Knowledge Discovery, 8(1), ( pp.53-87). (Pubitemid 39019971)
13. Inan.A., Saygyn.Y., Savas.E., Hintoglu.A.A, & Levi.A, (2006). Privacy preserving clustering on horizontally portioned data. In Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW'06).
14. Kantarcioglu, M. & Clifton, C. (2004). Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data. IEEE Transactions on Knowledge and Data Engineering, 16(9).
15. Lee,G. ,Chang ,C. & Chen, A.L.P .(2004). Hiding sensitive patterns in association rules mining. In Proceedings of the 28th Annual International Computer Software and Applications Conference (COMPSAC'04).
16. Lin, D. & Kedem, Z.M. (1998). Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set. In Proceedings of the 6th International Conference on Extending Database Technology (EDBT), Valencia .pp. 105-119. (Pubitemid 128044745)
17. Lin, D. & Kedem, Z.M. (2002). Pincer-Search : An Efficient Algorithm for Discovering the Maximum Frequent Set. IEEE Transactions on Knowledge and Data Engineering.14(3), 553-566. (Pubitemid 34669617)
18. Lindell, Y. & Pinkas, B.(2009). Secure Multiparty Computation for Privacy-Preserving Data Mining. In the Journal of Privacy and Confidentiality, 59-98.
19. Luo, W. (2006). An Algorithm for Privacy-preserving Quantitative Association Rules Mining. In Proceedings of the 2nd IEEE International Symposium.
20. Nedunchezhian, R. & Anbumani, K. (2006). Rapid Privacy Preserving Algorithm for Large Databases. International Journal of Intelligent Information Technologies. 2(1), 68-81. (Pubitemid 43788182)
21. Rajalakshmi, M., Purusothaman ,T. and Pratheeba, S. "Collusion-Free Privacy Preserving Data mining", International Journal of Intelligent Information Technologies (IJIIT), Vol.6, No. 4, pp.30-45, October 2010.
22. Saygin, Y., Verykios, S. & Elmagarmid, K. (2002). Privacy Preserving Association Rule Mining. In Proc. 12th International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems (RIDE'02), San Jose, USA, 151-158.
23. Stanley, R., Oliveira, M. & Zaýane, R. (2003). Algorithms for Balancing Privacy and Knowledge Discovery in Association Rule Mining. In Proceedings of the Seventh International Database Engineering and Applications Symposium (IDEAS'03), Hong Kong, China, 54-65.
24. Vaidya, J. & Clifton, C. (2002). Privacy Preserving Association Rule Mining in Vertically Partioned data. In Proceedings of the SIGKDD '02 Copyright ACM.
25. Vaidya, J. & Clifton, C. ( 2004). Privacy-Preserving Data Mining: Why, How, and When. IEEE Security and Privacy.
26. Verykios, S. Bertino, E. Provenza, I. Saygin, Y & Theodoridis, Y. (2004). State-of-the-Art in Privacy Preserving Data Mining. ACM SIGMOD Record, 33(1), 50-57. (Pubitemid 40290240)
27. Verykios, S., Elmagarmid, K., Bertino, E., Saygin, Y. & Dasseni, E. (2004).Association Rule Hiding. IEEE Transactions on Knowledge and Data Engineering, 16(4), 434-447.
28. Wang, S., Lee, Y., Billis, S. & Jafari, A. (2004). Hiding Sensitive Items in Privacy Preserving Association Rule Mining. IEEE International Conference on Systems, Man and Cybernetics.
29. Wu,Y., Chiang, C., & Chen, A. (2007). Hiding Sensitive Association Rules with Limited Side Effects. IEEE Transactions on Knowledge and DataEngineering, 19(1).