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A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 42 - Number 4 |
| Year of Publication: 2012 |
| Authors: Bhavana Abad (khivsara), Kinariwala S.a. |
10.5120/5680-7717
|
Bhavana Abad (khivsara), Kinariwala S.a. . A Novel approach for Privacy Preserving in Medical Data Mining using Sensitivity based anonymity. International Journal of Computer Applications. 42, 4 ( March 2012), 13-16. DOI=10.5120/5680-7717
Abstract
K-anonymity is one of the easy and efficient technique to achieve privacy preserving for sensitive data in many data publishing applications. In k-anonymity techniques, all tuples of releasing database are generalized to make it anonymize which lead to reduce the data utility and more information loss of publishing table. This paper firstly proposes a Sensitivity Based Tuple Anonymity Method. In this method first we consider the sensitivity of values in sensitive attribute and then only tuples having sensitive values are generalized, and the other tuples can be directly published. Experiment results on the Adult Database show the proposed methods not only can improve the accuracy of the publishing data, but also can preserve privacy
References
- Models and Algorithms : Privacy-Preserving Data Mining Springer 2008 : Charu Aggarwal , Philip Yu
- L. Sweeney. k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty Fuzziness Knowledge based Systems,10(5), pp 557-570. 2002
- N. Li, T. Li, S. Venkatasubramanian. t-Closeness: Privacy Beyond k-Anonymity andl-Diversity. ICDE 2007:106-115
- P. Samarati. Protecting respondents' identities in microdata release. IEEE Transactions on Knowledge and Data Engineering, 13(6):10101027. 2001
- A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam , "l-Diversity: Privacy beyond k-anonymity",In: Proceedings of the IEEE ICDE 2006
- K LeFevre, D DeWitt , R Ramakrishnan. Incognito:Efficient full domain k-anonymity Proceedings of the ACM SIGMOD International Conference on Management of Data. Baltimore , Maryland , 2005: 49-60 .
- R. Wong, J. Li, A. Fu, K. Wang. (?, k)-anonymity: an enhanced k-anonymity model For privacy preserving data publishing. KDD 2006:754-759
- B. Fung, K. Wang, P. Yu. Top-down specialization for information Conference on Data Engineering (ICDE05),pp:205-216
- R. Bayardo and R. Agrawal. Data privacy through optimal k- anonymity. In Proceedings of the 21st International onference on Data Engineering (ICDE), pp:217-228,Tokyo,Japan,2005
- J Pei , J Xu , Z. B Wang , W Wang, K Wang. Maintaining k- anonymity against incremental updates//Proceedings of the 19th International Conference on Scientific and Statistical Database
- U. C. Irvine Machine Learning Repository. http://www. ics. uci. edu/mlearn/mlrepository. html
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