Abstract
Existing privacy regulations together with large amounts of available data have created a huge interest in data privacy research. A main research direction is built around the k-anonymity property. Several shortcomings of the k-anonymity model have been fixed by new privacy models such as p-sensitive k-anonymity, l-diversity, α, k-anonymity, and t-closeness. In this paper we introduce the EnhancedPKClustering algorithm for generating p-sensitive k-anonymous microdata based on frequency distribution of sensitive attribute values. The p-sensitive k-anonymity model and its enhancement, extended p-sensitive k-anonymity, are described, their properties are presented, and two diversity measures are introduced. Our experiments have shown that the proposed algorithm improves several cost measures over existing algorithms.
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References
Aggarwal, G., Feder, T., Kenthapadi, K., Motwani, R., Panigrahy, R., Thomas, D., Zhu, A.: Anonymizing Tables. In: Proceedings of the ICDT, pp. 246–258 (2005)
Agrawal, R., Kiernan, J., Srikant, R., Xu, Y.: Hippocratic Databases. In: Proceedings of the VLDB, pp. 143–154 (2002)
Bayardo, R.J, Agrawal, R.: Data Privacy through Optimal k-Anonymization. In: Proceedings of the IEEE ICDE, pp. 217–228. IEEE Computer Society Press, Los Alamitos (2005)
Byun, J.W., Kamra, A., Bertino, E., Li, N.: Efficient k-Anonymity using Clustering Technique. CERIAS Tech. Report 2006-10 (2006)
Campan, A., Truta, T.M.: Extended P-Sensitive K-Anonymity. Studia Universitatis Babes-Bolyai Informatica 51(2), 19–30 (2006)
Campan, A., Truta, T.M., Miller, J., Sinca, R.A.: Clustering Approach for Achieving Data Privacy. In: Proceedings of the International Data Mining Conference (2007)
HIPAA.: Health Insurance Portability and Accountability Act (2002), Available online at: http://www.hhs.gov/ocr/hipaa
ICD9.: International Classification of Diseases. Available online at: http://icd9cm.chrisendres.com/index.php
Iyengar, V.: Transforming Data to Satisfy Privacy Constraints. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 279–288. ACM Press, New York (2002)
Lambert, D.: Measures of Disclosure Risk and Harm. Journal of Official Statistics 9, 313–331 (1993)
LeFevre, K., DeWitt, D., Ramakrishnan, R.: Incognito: Efficient Full-Domain K-Anonymity. In: Proceedings of the ACM SIGMOD, pp. 49–60. ACM Press, New York (2005)
LeFevre, K., DeWitt, D., Ramakrishnan, R.: Mondrian Multidimensional K-Anonymity. In: Proceedings of the IEEE ICDE, vol. 25 (2006)
Li, N., Li, T., Venkatasubramanian, S.: T-Closeness: Privacy Beyond k-Anonymity and l-Diversity. In: Proceedings of the IEEE ICDE (2007)
Machanavajjhala, A., Gehrke, J., Kifer, D.: L-Diversity: Privacy beyond K-Anonymity. In: Proceedings of the IEEE ICDE, vol. 24 (2006)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, UC Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Samarati, P.: Protecting Respondents Identities in Microdata Release. IEEE Transactions on Knowledge and Data Engineering 13(6), 1010–1027 (2001)
Sweeney, L.: k-Anonymity: A Model for Protecting Privacy. International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10(5), 557–570 (2002)
Sweeney, L.: Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems 10(5), 571–588 (2002)
Truta, T.M., Bindu, V.: Privacy Protection: P-Sensitive K-Anonymity Property. In: Proceedings of the Workshop on Privacy Data Management, In Conjunction with IEEE ICDE, vol. 94 (2006)
Truta, T.M., Campan, A.: K-Anonymization Incremental Maintenance and Optimization Techniques. In: Proceedings of the ACM SAC, pp. 380–387. ACM Press, New York (2007)
Winkler, W.: Matching and Record Linkage. In: Business Survey Methods, Wiley, Chichester (1995)
Wong, R.C-W., Li, J., Fu, A.W-C., Wang, K.: (α, k)-Anonymity: An Enhanced k-Anonymity Model for Privacy-Preserving Data Publishing. In: Proceedings of the ACM KDD, pp. 754–759. ACM Press, New York (2006)
Xiao, X., Tao, Y.: Personalized Privacy Preservation. In: Proceedings of the ACM SIGMOD, pp. 229–240. ACM Press, New York (2006)
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Truta, T.M., Campan, A., Meyer, P. (2007). Generating Microdata with P-Sensitive K-Anonymity Property. In: Jonker, W., Petković, M. (eds) Secure Data Management. SDM 2007. Lecture Notes in Computer Science, vol 4721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75248-6_9
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DOI: https://doi.org/10.1007/978-3-540-75248-6_9
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