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KAMP：Preserving k-anonymity for Combinations of Patterns
|關鍵字:||k-匿名化;k-anonymity;隱私保護;Re-identification by linking攻擊;Privacy preserving;Re-identification by linking attacks||出版社:||電機工程學系所||引用:||R. Agrawal, T. Imielinski, A. Swami, “Mining association rules between sets of items in large databases,” In Proc. of the ACM SIGMOD Conference on Management of Data, 207–216, 1993. R. Agrawal and R. Srikant, “Fast Algorithm for Mining Association Rules in Large Databases,” In Proc. of the 20th International Conference on Very Large Data Bases, 487-499, 1994. L. Sweeney, “Achieving k-anonymity privacy protection using generalization and suppression,” International Journal of Uncertainty, Fuzziness, and Knowledge-based Systems, 10(5):571–588, 2002. V. S. Verykios, E. Bertino, I. N. Fovino, L. P. Provenza, Y. Saygin, and Y. Theodoridis, “State-of-the-art in privacy preserving data mining,” ACM SIGMOD Record, 3(1):50–57, 2004. C. Clifton, M. Kantarcioglu, J. Vaidya, X. Lin, and M. Y. Zhu, “Tools for privacy preserving distributed data mining,” ACM SIGKDD Explorations Newsletter, 4(2):28–34, 2002. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke, “Privacy preserving mining of association rules,” In Proc. of 8th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 217–228, 2002. A. Inan, S. V. Kaya, Y. Saygin, E. Savas, A. A. Hintoglu, and A. Levi, “Privacy preserving clustering on horizontally partitioned data,” Data & Knowledge Engineering (DKE), 63(3):646–666, 2007. P. Zhang, Y. Tong, S. Tang, and D. Yang, “Privacy-preserving naive bayes classifier,” Lecture Notes in Computer Science, 3584, 2005. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi, “Anonymity preserving pattern discovery,” International Journal on Very Large Data Bases (VLDBJ), 17(4):703–727, 2008. R.G. Pensa, A. Monreale, F. Pinelli, D. Pedreschi, “Pattern-preserving k-anonymization of sequences and its application to mobility data mining,” In Proc. International Workshop on Privacy in Location Based Applications, 2008. L. Sweeney, “k-Anonymity: A model for protecting privacy,” International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, 10(5):557–570, 2002. G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy, D. Thomas, and A. Zhu, “Anonymizing tables,” In Proc. of the 10th International Conference on Database Theory (ICDT), 246–258, 2005. P. Kalnis, G. Ghinita, K. Mouratidis, and D. Papadias, “Preventing Location-Based Identity Inference in Anonymous Spatial Queries,” IEEE Trans. Knowledge and Data Eng., 19(12):1719-1733, 2007. B. Krishnamachari, G. Ghinita, and P. Kalnis, “Privacy-Preserving Publication of User Locations in the Proximity of Sensitive Sites,” In Proc. SSDBM, 2008.  F. Bonchi, F. Giannotti, and D. Pedreschi, “Blocking anonymity threats raised by frequent itemset mining,” In Proc. of the 5th IEEE International Conference on Data Mining (ICDM), 561-564, 2005. C.-H. Tai, P. S. Yu, and M.-S. Chen, “k-Support Anonymity based on Pseudo Taxonomy for Outsourcing of Frequent Itemset Mining,” In Proc. of the 16th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), 473-482, 2010. R.G. Pensa, A. Monreale, F. Pinelli, D. Pedreschi, “Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining,” In Proc. International Workshop on Privacy in Location-Based Applications (PiLBA), 2008. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi, ”k-anonymous patterns,” In Proc. of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 10–21, 2005. K. Bharath, G. Ghinita, and P. Kalnis, “Privacy-Preserving Publication of User Locations in the Proximity of Sensitive Sites,” In Proc. 20th International Conference on Scientific and Statistical Database Management (SSDBM), 2008. M. Terrovitis, N. Mamoulis, and P. Kalnis, “Privacy-preserving anonymization of set-valued data,” In Proc. of the VLDB Endowment, 1(1):115–125, 2008. BMS-WebView-1, the KDD-Cup2000, http://www.sigkdd.org/kddcup/index.php?section=2000&method=data. IBM Quest, http://www.almaden.ibm.com/cs/quest/syndata.html.||摘要:||
現今，因企業或組織所生成及累積的資料越具龐大，將資料委外儲存、管理或進行知識挖掘已成為一種典範。然而，這些資料雖蘊含著有價值的知識，但同時亦潛藏著大量敏感資訊，極可能成為個人隱私上的一大隱憂。為避免發佈共享或委外之資料遭受交叉辨識(Re-identification by linking)攻擊而導致個人身份曝光，因此資料在發佈之前必須先經過隱私保護處理。
As huge data are increasingly generated and accumulated, outsourcing data for storage, management, and knowledge discovery is becoming a paradigm. However, given that a lot of sensitive information and valuable knowledge are hidden in data, the outsourcing of data is vulnerable to privacy crises and leads to demands for generalization or suppression techniques to protect data from re-identification attacks.
Differing from previous works that aim at satisfying the k-anonymity on individual patterns or individual items, we propose the k-anonymity of multi-pattern (KAMP) problem to protect data from re-identifying users by using a combination of patterns and also propose the KAMP-p1 algorithm to generalize and suppress data. To study the effectiveness of the proposed algorithm, we conduct experiments on a synthetic and a small real dataset. The experimental results show that KAMP-p1 algorithm can satisfy k-anonymity while preserving many patterns in order to retain useful knowledge for decision making.
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