The week's pick
Reseach Article
Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 6 - Number 10 |
| Year of Publication: 2010 |
| Authors: Sumana M, Dr Hareesh K S |
10.5120/1113-1457
|
Sumana M, Dr Hareesh K S . Anonymity: An Assessment and Perspective in Privacy Preserving Data Mining. International Journal of Computer Applications. 6, 10 ( September 2010), 1-5. DOI=10.5120/1113-1457
Abstract
Privacy Preserving Data mining techniques depends on privacy, which captures what information is sensitive in the original data and should therefore be protected from either direct or indirect disclosure. Secrecy and anonymity are useful ways of thinking about privacy. This privacy should be measureable and entity to be considered private should be valuable. In this paper, we discuss the various anonymization techniques that can be used for privatizing data. The goal of anonymization is to secure access to confidential information while at the same time releasing aggregate information to the public. The challenge in each of the techniques is to protect data so that they can be published without revealing confidential information that can be linked to specific individuals. Also protection is to be achieved with minimum loss of the accuracy sought by database users. Different approaches of anonymization have been discussed and a comparison of the same has been provided.
References
- Han Jian-min, Yu Hui-qun, Yu Juan, Cen Ting-ting “A Complete (α,k)-Anonymity Model for Sensitive Values Individuation Preservation”, 2008 IEEE DOI 10.1109/ISECS.2008.92
- Latanya Sweeney “Achieving k-anonymity Privacy Protection Using Generalization and Suppression”,May 2002, International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, 10 (5), 2002; 571-588.
- V. Ciriani, S. De Capitani di Vimercati, S. Foresti, and P. Samarati, k-Anonymity, Springer US, Advances in Information Security (2007).
- Li Liu, Murat Kantarcioglu and Bhavani Thuraisingham, Privacy Preserving Decision Tree Mining from Perturbed Data, Proceedings of the 42nd Hawaii International Conference on System Sciences – 2009
- Charu C Aggarwal, Philip S Yu, Privacy Preserving Data Mining: Models and Algorithms, Springer Publication, 2007.
- Ninghui Li, Tiancheng Li, Suresh Venkatasubramanian, t-Closeness: Privacy Beyond k-Anonymity and ℓ-Diversity, Citiseer 2007
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