Abstract
Preserving a sensitive data has become a great challenge in the area of research under data privacy. There are popular approaches such as k-anonymity, t-closeness [1] and l-diversity which are effective measures for preserving privacy. These techniques lead to solving many of the privacy issues. But all these measures suffer from one or the other types of attacks. To minimize these attacks, a new measure called p-sensitive, t-closeness is introduced. This measure will preserve the sensitive data by distributing different values of sensitive attribute according to t-closeness approach by introducing p-sensitivity, by minimizing attacks and improving the efficiency and utility of the data. This technique is termed as p-sensitive, t-closeness which satisfy p-sensitivity and t-closeness for a table by relaxing the threshold value t, so that; it will satisfy the p sensitivity to overcome many of limitations of previous approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Li, N., Li, T., Venkatasubramanian, S.: Closeness: A New Privacy Measure for Data Publishing. IEEE Trans. Knowl. Data Eng. 22(7), 943–956 (2010)
Sweeney, L.: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge based Systems, 557–570 (2002)
Truta, T.M., Vinay, B.: Privacy protection: p-Sensitive k-anonymity property. In: Proceedings of the 22nd on Data Engineering Workshops. IEEE Computer Society, Washington, DC (2006)
Domingo-Ferrer, J., Torra, V.: A Critique of k-Anonymity and Some of Its Enhancements, pp. 990–993 (2008)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proc. SIGMOD 2000, pp. 439–450 (2000)
Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving dat mining. In: Proc. of ACM SIGMOD, pp. 50–57 (2004)
Truta, T.M., Vinay, B.: Privacy protection: p-sensitive k-anonymity property. In: 2nd International Workshop on Privacy Data Management PDM 2006, p. 94. IEEE Computer Society, Berlin (2006)
Vijayarani, S., Tamilarasi, A., Sampoorna, M.: Analysis of Privacy Preserving K-Anonymity Methods and Techniques. In: Proceedings of the International Conference on Communication and Computational Intelligence 2010, Kongu Engineering College, Perundurai, Erode, T.N.,India, December 27-29, pp. 540–545 (2010)
Wu, Y., Ruan, X., Liao, S., Wang, X.: P-Cover K-anonymity model for Protecting Multiple Sensitive Attributes. In: The 5th International Conference on Computer Science & Education, Hefei, China, August 24-27 (2010)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, UC Irvine (1998), www.ics.uci.edu/~mlearn/MLRepository.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer India
About this paper
Cite this paper
Sowmyarani, C.N., Srinivasan, G.N., Sukanya, K. (2013). A New Privacy Preserving Measure: p-Sensitive, t-Closeness. In: Kumar M., A., R., S., Kumar, T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0740-5_7
Download citation
DOI: https://doi.org/10.1007/978-81-322-0740-5_7
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0739-9
Online ISBN: 978-81-322-0740-5
eBook Packages: EngineeringEngineering (R0)