Abstract
Privacy concerns on sensitive data are becoming indispensable in data publishing and knowledge discovering. The k-anonymization provides a way to protect the sensitivity without fabricating the data records. However, the anonymity can be breached by leveraging the associations between quasi-identifiers and sensitive attributes.
In this paper, we model the possible privacy breaches as Q-S associations using association and dissociation rules. We enhance the common k-anonymization methods by evaluating the Q-S associations. Moreover, we develop a greedy algorithm for rule hiding in order to remove all the Q-S associations in every anonymity-group. Our method can not only protect data from the privacy breaches but also minimize the data loss. We also make a comparison between our method and one of the common k-anonymization strategies.
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References
Agrawal, R., Srikant, S.: Privacy-preserving data mining. In: Proc. of the ACM SIGMOD Conference on Management of Data (2000)
Sweeney, L.: Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty 10(5), 571–588 (2002)
Meyerson, A., Williams, R.: On the complexity of optimal k-anonymity. In: Proc. of the 23th ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2004)
LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: Efficient fulldomain k-anonymity. In: Proc. of the 2005 ACM SIGMOD international conference on Management of data (2005)
Hundpool, A., Willenborg, L.: Mu-argus and tau-argus: Software for statistical disclosure control. In: Proc. of the 3rd International Seminar on Statistical Confidentiality (1996)
lyengar, V.S.: Transforming data to satisfy privacy constraints. In: Proc. of the 8th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, New York (2002)
Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proc. of the 21th International Conference on Data Engineering (2005)
Wang, K., Yu, P., Chakraborty, S.: Bottom-up generalization a data mining solution to privacy protection. In: Proc. of the 4th IEEE International Conference on Data Mining (2004)
Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: l-diversity privacy beyond k-anonymity. In: Proc. of the 22th International Conference on Data Engineering (2006)
Nergiz, M.E., Clifton, C.: Thoughts on k-anonymization. In: Proc. of the 22th International Conference on Data Engineering Workshops (2006)
Xiao, X., Tao, Y.: Personalized privacy preservation. In: Proc. of the 2006 ACM SIGMOD international conference on Management of data (2006)
Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association rule hiding. IEEE Transactions on Knowledge and Data Engineering 16(4), 434–447 (2004)
Oliveira, S.R.M., Zaiane, O.R.: A unified framework for protecting sensitive association rules in business collaboration. International Journal of Business Intelligence and Data 1(3), 247–287 (2006)
Savasere, A., Omiecinski, E., Navathe, S.: An efficient algorithm for mining association rules in large databases. In: Proc. of the 21th International Conference on Very Large Data Bases (1995)
Wu, X., Zhang, C., Zhang, S.: Mining both positive and negative association rules. In: Proc. of the 19th International Conference on Machine Learning (2002)
Hettich, S., Bay, S.D.: The uci kdd archive. Univeristy of California, Irvine, Department of Information and Computer Science (1999)
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Yang, W., Huang, S. (2007). k-Anonymization Without Q-S Associations. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_77
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DOI: https://doi.org/10.1007/978-3-540-72524-4_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
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