Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

This is an Open Access publication published under CSC-OpenAccess Policy.
Improved Slicing Algorithm For Greater Utility In Privacy Preserving Data Publishing
Ajinkya Abhimanyu Dhaigude, Preetham Kumar
Pages - 14 - 21     |    Revised - 10-09-2014     |    Published - 10-10-2014
Volume - 5   Issue - 2    |    Publication Date - October 2014  Table of Contents
Data Anonymization, Privacy Preservation, Data Mining, Slicing.
Several algorithms and techniques have been proposed in recent years for the publication of sensitive microdata. However, there is a trade-off to be considered between the level of privacy offered and the usefulness of the published data. Recently, slicing was proposed as a novel technique for increasing the utility of an anonymized published dataset by partitioning the dataset vertically and horizontally. This work proposes a novel technique to increase the utility of a sliced dataset even further by allowing overlapped clustering while maintaining the prevention of membership disclosure. It is further shown that using an alternative algorithm to Mondrian increases the efficiency of slicing. This paper shows though workload experiments that these improvements help preserve data utility better than traditional slicing.
CITED BY (1)  
1 Shyamala, V. S., & Christopher, T. (2015). Managing Privacy of Sensitive Attributes Using MFSARNN Clustering with Optimization Technique. International Review on Computers and Software (IRECOS), 10(9), 907-911.
1 Google Scholar
2 CiteSeerX
3 refSeek
4 Scribd
5 SlideShare
6 PdfSR
1 P. Samarati, “Protecting respondents’ identities in microdata release,” IEEE Trans. on Knowl. and Data Eng., vol. 13, no. 6, pp. 1010–1027, 2001.
2 L. Sweeney, “Achieving k-anonymity privacy protection using generalization and suppression,” International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, vol. 10, no. 6, pp. 571–588, 2002.
3 X. Xiao and Y. Tao, “Anatomy: Simple and effective privacy preservation,” in Proceedings of the 32Nd International Conference on Very Large Data Bases, ser. VLDB ’06, 2006, pp. 139–150.
4 T. Li, N. Li, J. Zhang, and I. Molloy, “Slicing: A new approach for privacy preserving data publishing,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, pp. 561 – 574, 2012.
5 C. C. Aggarwal, “On k-anonymity and the curse of dimensionality,” in Proceedings of the 31st International Conference on Very Large Data Bases, 2005, pp. 901–909.
6 K. LeFevre, D. J. DeWitt, and R. Ramakrishnan, “Mondrian multidimensional k-anonymity,” in Proceedings of the 22nd International Conference on Data Engineering, 2006.
7 F. Bonchi, A. Gionis, and A. Ukkonen, “Overlapping correlation clustering,” in Proceedings of the 2011 IEEE 11th International Conference on Data Mining, 2011, pp. 51–60.
8 Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “L-diversity: Privacy beyond k-anonymity,” ACM Trans. Knowl. Discov. Data, vol. 1, no. 1, 2007.
9 J. H. Friedman, J. L. Bentley, and R. A. Finkel, “An algorithm for finding best matches in logarithmic expected time,” ACM Trans. Math. Softw., vol. 3, no. 3, pp. 209–226, 1977.
10 Internet: http://archive.ics.uci.edu/ml/datasets/Adult, [Mar. 14, 2014 ].
Mr. Ajinkya Abhimanyu Dhaigude
Manipal University - India
Dr. Preetham Kumar
Manipal Institute of Technology - India