Trade-off between Utility and Security using Group Privacy Threshold Sanitization
Keywords:
Restricted patterns, Sanitization, Sensitive transactions, Group-based ThresholdAbstract
Data mining is a well-known technique for automatically and intelligently extracting useful information or knowledge from a large amount of data, but it can also disclose sensitive information of an individual or a company. This promotes the need for privacy preserving data mining which is becoming an increasingly important field of research and many researchers have proposed techniques for handling this concept. However, most of the privacy preserving data mining approaches concentrate on fixed disclosure threshold strategy for all sensitive information. This article proposes an approach for group-based threshold strategy which may help facilitate to use varying sensitivity level for the information to be hidden.
References
Richard Yevich, "Data Mining," In: Joyce Bischoff, Ted Alexander and Sid Adelman (editors). DataWarehouse: Practical Advice from the Experts. Upper Saddle River, N.J.: Prentice Hall, (1997).
A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrk, “Privacy Preserving Mining of Association Rules”, In Proceedings the 8th ACM SIGKDD International Conference on Knowledge Discovery in Databases and Data Mining, pp.217-228, 2002.
S. Rizvi, J. Haritsa, “Maintaining Data Privacy in Association Rule Mining”, In Proceedings the 28th International Conference on Very Large Data Bases, pp.682-693, 2002.
L. Sweeney, “k-Anonymity: A Model for Protecting Privacy”, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, vol.10, no.5, pp.557-570,2002.
X.K. Xiao, Y.F. Tao, “Personalized Privacy Preservation”, In Proceedings of the ACM Conference on Management of Data (SIGMOD), pp.229-240, 2006.
M. Kantarcioglu, C. Clifton, “Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data”, IEEE Transactions on Knowledge and Data Engineering, vol.16, no.9, pp.1026-1037, 2004.
Lindell, Yehuda, Pinkas, “Privacy preserving data mining”, In Proceedings of the Advances in Cryptology–CRYPTO, pp.36–54,2000.
J. Vaidya, C. Clifton, “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.639-644, 2002.
Cynthia Selvi P., Mohamed Shanavas A.R., “Towards Information Privacy Using Transaction-Based Maxcover Algorithm”, World Applied Sciences Journal 29 (Data Mining and Soft Computing Techniques): 06-11, 2014, ISSN 1818-4952, © IDOSI Publications, 2014, DOI:10.5829/ idosi.wasj.2014.29. dmsct.2, Pages. 06-11
The Dataset used in this work for experimental analysis was generated using the generator from IBM Almaden Quest research group and is publicly available from http://fimi.ua.ac.be/data/.
Pavon J, Viana S, Gomez S, “Matrix Apriori: speeding up the search for frequent patterns,” Proc. 24th IASTED International Conference on Databases and Applications, 2006, pp. 75-82.
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