Abstract
Mining frequent itemsets from huge amounts of data is an important issue in data mining, with the retrieved information often being commercially valuable. However, some sensitive itemsets have to be hidden in the database due to privacy or security concerns. This study aimed to secure sensitive information contained in patterns extracted during association-rule mining. The proposed approach successfully hides sensitive itemsets whilst minimizing the impact of the sanitization process on nonsensitive itemsets. Our approach ensures that any modification to the database is controlled according to its impact on the sanitized database. The results of simulations demonstrate the benefits of our approach.
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References
Atallah, M., Bertino, E., Elmagarmid, A., Ibrahim, M., Verykios, V.: Disclosure Limitation of Sensitive Rules. In: IEEE Workshop on Knowledge and Data Engineering Exchange, pp. 45–52 (1999)
Ayubia, S., Muyebab, M.K., Baraania, A., Keanec, J.: An Algorithm to Mine General Association Rules from Tabular Data. Information Sciences 179, 3520–3539 (2009)
Divanis, A.G., Verykios, V.S.: An Integer Programming Approach for Frequent Itemset Hiding. In: ACM International Conference on Information and Knowledge Management, pp. 748–757 (2006)
Divanis, A.G., Verykios, V.S.: Exact Knowledge Hiding Through Database Extension. IEEE Transactions on Knowledge and Data Engineering 21, 699–713 (2009)
Gueret, C., Prins, C., Sevaux, M.: Applications of Optimization with Xpress-MP. Dash Optimization Ltd. (2002)
Lee, G., Chang, C.Y., Chen, A.L.P.: Hiding Sensitive Patterns in Association Rules Mining. In: Annual International Conference on Computer Software and Applications, pp. 424–429 (2004)
Menon, S., Sarkar, S., Mukherjree, S.: Maximizing Accuracy of Shared Databases when Concealing Sensitive Patterns. Information Systems Research 16, 256–270 (2004)
Oliveira, S.R.M., Zaïane, O.R.: Privacy Preserving Frequent Itemset Mining. In: IEEE International Conference on Privacy, Security and Data Mining, pp. 43–54 (2002)
Oliveira, S.R.M., Zaïane, O.R.: Algorithms for Balancing Privacy and Knowledge Discovery in Association Rule Mining. In: Database Engineering and Applications Symposium, pp. 54–63 (2003)
Oliveira, S.R.M., Zaïane, O.R.: Protecting Sensitive Knowledge By Data Santization. In: IEEE International Conference on Data Mining, pp. 613–616 (2003)
Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach, 2nd edn. Prentice-Hall, Englewood Cliffs (2003)
Sun, X., Yu, P.S.: Hiding Sensitive Frequent Itemsets by a Border-Based Approach. Computer Science and Engineering 1, 74–97 (2007)
Sun, X., Yu, P.S.: A Border-Based Approach for Hiding Sensitive Frequent Itemsets. In: IEEE International Conference on Data Mining, pp. 426–433 (2005)
Verykios, V.S., Elmagarmid, A.K., Bertino, E., Saygin, Y., Dasseni, E.: Association Rule Hiding. IEEE Transactions on Knowledge and Data Engineering 16, 434–447 (2004)
Wang, E.T., Lee, G., Lin, Y.T.: A Novel Method for Protecting Sensitive Knowledge in Association Rules Mining. In: IEEE Annual International Computer Software and Applications Conference, pp. 511–516 (2005)
Wang, E.T., Lee, G.: An Efficient Sanitization Algorithm for Balancing Information Privacy and Knowledge Discovery in Association Patterns Mining. Data and Knowledge Engineering 65, 463–484 (2008)
Wu, Y.H., Chiang, C.C., Chen, A.L.P.: Hiding Sensitive Association Rules with Limited Side Effects. IEEE Transactions on Knowledge and Data Engineering 19, 29–42 (2007)
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Lee, G., Chen, YC., Peng, SL., Lin, JH. (2011). Solving the Sensitive Itemset Hiding Problem Whilst Minimizing Side Effects on a Sanitized Database. In: Chang, RS., Kim, Th., Peng, SL. (eds) Security-Enriched Urban Computing and Smart Grid. SUComS 2011. Communications in Computer and Information Science, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23948-9_13
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DOI: https://doi.org/10.1007/978-3-642-23948-9_13
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