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An Empirical Study on Privacy-Preserving Models

M.Giri*, S. Madhumitha**

* Professor and Head, Department of CSE, Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, India.

** M.Tech Scholar, Department of CSE, Sreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, India.

Periodicity:November - January'2014
DOI : https://doi.org/10.26634/jes.2.4.2805

Abstract

In real world many organizations deal with large amount of data, which are the private information collected from individuals. They need to provide security measures to the private data, to provide the results without revealing private information. Many individuals are afraid of exposing their own information and give false inputs and the organizations should be careful about when and where to expose the privacy information and provide security controls. This is how the privacy preserving data mining has become more popular in recent years. In this paper we discuss about various privacy preserving data mining models and also provide comparative study on it. Measuring different techniques, the authors propose that secure multiparty computation mechanism is the best solution for protecting the private information.

Keywords

Data Mining, Privacy Preserving, Secure Multiparty Computation.

How to Cite this Article?

Giri.M., and Madhumitha.S. (2014). An Empirical Study On Privacy-Preserving Models. i-manager’s Journal on Embedded Systems, 2(4), 31-36. https://doi.org/10.26634/jes.2.4.2805

References

[1]. M. Kantarcioglu and W. Jiang. (2013). “Incentive Compatible Privacy Preserving Data Analysis”. IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 6, pages 1323- 1335,June.

[2]. J. Wang, Y. Zhao, J. Le, Y. Luo. A (2009). “Survey on Privacy Preserving Data Mining”. First International Workshop on Database Technology and Applications, pages 111-114,

[3]. X. Ge and J. Zhu. (2011). “New Fundamental Technologies from Data Mining” edited by K. Funatsu, In Tech. Privacy Preserving Data Mining, Vol. 29, pages 535- 560, January.

[4]. Y. Shoham and M. Tennenholtz. “Non- Cooperative: Boolean functions with correctness and exclusivity”. Pages 1-21.

[5]. F. Al-Yarimi and S. Minz. (2012). “Multilevel Privacy Preserving in Distributed Environment using Cryptographic Technique”. Proceedings of the World Congress on Engineering, Vol. I.

[6]. B. Pinkas and Y. Lindell. (2009). “Secure Multi-party Computation for Privacy Preserving Data Mining”. The Journal of Privacy and Confidentiality, No. 1, pages 59- 98.

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