Anonymizing Transaction Data to Eliminate Sensitive Inferences
Publishing transaction data containing individuals’ activities may risk privacy breaches, so the need for anonymizing such data before their release is increasingly recognized by organizations. Several approaches have been proposed recently to deal with this issue, but they are still inadequate for preserving both data utility and privacy. Some incur unnecessary information loss in order to protect data, while others allow sensitive inferences to be made on anonymized data. In this paper, we propose a novel approach that enhances both data utility and privacy protection in transaction data anonymization. We model potential inferences of individuals’ identities and their associated sensitive transaction information as a set of implications, and we design an effective algorithm that is capable of anonymizing data to prevent these sensitive inferences with minimal data utility loss. Experiments using real-world data show that our approach outperforms the state-of-the-art method in terms of preserving both privacy and data utility.
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- 1.Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: VLDB’94, pp. 487–499 (1994)Google Scholar
- 2.Barbaro, M., Zeller, T.: A face is exposed for aol searcher no. 4417749. New York Times (August 2006)Google Scholar
- 3.Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: A survey on recent developments. ACM Comp. Surv. 42(4) (2010)Google Scholar
- 4.Ghinita, G., Tao, Y., Kalnis, P.: On the anonymization of sparse high-dimensional data. In: ICDE ’08, pp. 715–724 (2008)Google Scholar
- 5.Gkoulalas-Divanis, A., Verykios, V.S.: Exact knowledge hiding through database extension. IEEE TKDE 21(5), 699–713 (2009)Google Scholar
- 6.He, Y., Naughton, J.F.: Anonymization of set-valued data via top-down, local generalization. PVLDB 2(1), 934–945 (2009)Google Scholar
- 7.Loukides, G., Gkoulalas-Divanis, A., Malin, B.: Coat: Constraint-based anonymization of transactions. CoRR, abs/0912.2548, Technical Report (2009)Google Scholar
- 8.Samarati, P.: Protecting respondents identities in microdata release. IEEE TKDE 13(9), 1010–1027 (2001)Google Scholar
- 9.Terrovitis, M., Mamoulis, N., Kalnis, P.: Privacy-preserving anonymization of set-valued data. PVLDB 1(1), 115–125 (2008)Google Scholar
- 10.Xu, Y., Wang, K., Fu, A.W.-C., Yu, P.S.: Anonymizing transaction databases for publication. In: KDD ’08, pp. 767–775 (2008)Google Scholar
- 11.Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: KDD ’01, pp. 401–406 (2001)Google Scholar