Preserving Privacy in Data Mining

Authors

  • Hemlata  Department of Computer Science, Maharshi Dayanand University, Rohtak, Haryana, India

Keywords:

Data Mining, Privacy Preserving Data Mining, PPDM Framework.

Abstract

Data Mining means the process of deriving new knowledge, rules and patterns from the existing database. Knowledge is extracted from unstructured, large amount of data for analysis. The process is also known as knowledge discovery. The derived data or the patterns provide valuable information in decision-making process and business strategy. The results of Data Mining should not reveal the sensitive data of users. The Data Mining techniques for preserving the privacy of data from malicious users are termed as Privacy Preserving Data Mining Techniques. This paper provides a review of privacy preserving Data mining architecture. It also presents the bands of Privacy Preserving Objectives. Approaches of Privacy preserving are summarized in the paper. This paper is intended for the researchers and scientists who work in the field of Privacy preserving Data Mining.

References

  1. K Thearling, “Data Mining and Privacy: A Conflict in Making”, DS, November 1998.
  2. R Agrawal and R. Srikant. “Privacy Preserving Data Mining”, ACM SIGMOD Conference on Management of Data, pp: 439-450, 2000
  3. Y Lindell and B. Pinkas, “Privacy Preserving Data Mining”, Journal of Cryptology, 15(3), pp.36-54, 2000.
  4. C Clifton, M. Kantarcioglu, and J. Vaidya. “Defining Privacy for Data Mining”, Proceedings of the National Science Foundation Workshop on Next Generation Data Mining, 2002. pp. 126-133.
  5. Hemlata, Gulia, P. (2018). DCI3 Model for Privacy Preserving in Big Data. In Big Data Analytics (pp. 351-362). Springer, Singapore.
  6. S R. M. Oliveira and Osmar R. Zaiane, “Toward Standardization in Privacy-Preserving Data Mining”, DMSSP 2004 (In conjunction with SIGKDD 2004).
  7. D Agrawal and C. Aggarwal, “On the Design and Quantification of Privacy Preserving Data Mining Algorithms”, PODS 2001. pp: 247-255.
  8. A Evfimievski, R. Srikant, R. Agrawal and J. Gehrke, “Privacy Preserving Mining of Association Rules”, SIGKDD 2002. pp. 217- 228
  9. Hemlata, Gulia, Preeti. "Novel Algorithm for PPDM of Vertically Partitioned Data." International Journal of Applied Engineering Research 12.12 (2017): 3090-3096.
  10. S. Rizvi and J. Haritsa, “Maintaining Data Privacy in Association Rule Mining”, VLDB 2002. pp: 682-693.
  11. W. Du and Z. Zhan, “Using Randomized Response Techniques for Privacy Preserving Data Mining”, SIGKDD 2003. pp. 505-510.
  12. S. Agrawal and J. Haritsa, “On Addressing Efficiency Concerns in Privacy-Preserving Mining”, DASFAA 2004. pp. 113-124.
  13. Hemlata and Dr. Preeti Gulia, “Techniques and Algorithms of PPDM”, International Journal for Scientific Research & Development, Vol. 3, Issue 04, 2015, pp. 3484-3487.
  14. Stanley, R. M. O. and R. Z Osmar, “Towards Standardization in Privacy Preserving Data Mining”, Published in Proceedings of 3rd Workshop on Data Mining Standards, WDMS’ 2004, USA, p.7-17.
  15. S.V. Vassilios , B. Elisa, N.F. Igor, P.P. Loredana, S. Yucel and T. Yannis, 2004, “State of the Art in Privacy Preserving Data Mining” Published in SIGMOD Record, 33, 2004, pp: 50-57.
  16. Elisa, B., N.F. Igor and P.P. Loredana. “A Framework for Evaluating Privacy Preserving Data Mining Algorithms”, Published by Data Mining Knowledge Discovery, 2005, pp.121-154.
  17. Philip Chan, “An Extensible Meta Learning Approach for scalable and Accurate Inductive Learning”, PhD Thesis, Department of Computer Sciences, Columbia University, New York, NY, 1996
  18. Philip Chan, “On the accuracy of meta-learning for scalable data mining”. Journal of intelligent Information Systems, 8:5-28, 1997.
  19. Andreas Prodromidis, Philip Chan, and Salvatore Stolfo, : “Metalearning in distributed data mining systems: Issues and approaches”. In “Advances in Distributed and Parallel Knowledge Discovery”, AAAI/MIT Press, September 2000.
  20. S.V. Vassilios , B. Elisa, N.F. Igor, P.P. Loredana, S. Yucel and T. Yannis, 2004, “State of the Art in Privacy Preserving Data Mining” Published in SIGMOD Record, 33, 2004, pp: 50-57.
  21. Gayatri Nayak, Swagatika Devi, "A survey on Privacy Preserving Data Mining: Approaches and Techniques", International Journal of Engineering Science and Technology, Vol. 3 No. 3, 2127-2133, 2011.
  22. Aggarwal C, Philip S Yu, "A General Survey of Privacy-Preserving Data Mining Models and Algorithms", Springer Magazine, XXII, 11-52, 2008.
  23. J. Vaidya, C. Clifton, “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 639–644, 2002.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-06-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Hemlata, " Preserving Privacy in Data Mining, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.2079-2083, March-April-2018.