Abstract
High-Utility Itemset Mining (HUIM) considers both quantity and profit factors to measure whether an item or itemset is a profitable product. With the rapid growth of security considerations, privacy-preserving utility mining (PPUM) has become a critical issue in HUIM. In this paper, an efficient algorithm is proposed to minimize side effects in the sanitization process for hiding sensitive high utility itemsets. Three similarity measurements are also designed as the new standard used in PPUM. Experiments are also conducted to show the performance of the designed algorithm in terms of general side effects in PPDM and the new defined measurements in PPUM.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: The International Conference on Very Large Data Bases, pp. 487–499 (1994)
Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM SIGMOD Record 29(2), 439–450 (2000)
Amiri, A.: Dare to share: Protecting sensitive knowledge with data sanitization. Decision Support Systems 43(1), 181–191 (2007)
Atallah, M., Elmagarmid, A., Ibrahim, M., Bertino, E., Verykios, V.: Disclosure limitation of sensitive rules. In: The Workshop on Knowledge and Data Engineering Exchange, pp. 45–52 (1999)
Bertino, E., Fovino, I.N., Provenza, L.P.: A framework for evaluating privacy preserving data mining algorithms. Data Mining and Knowledge Discovery 11(2), 121–154 (2005)
Chen, M.S., Han, J., Yu, P.S.: Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8(6), 866–883 (1996)
Dasseni, E., Verykios, V.S., Elmagarmid, A.K., Bertino, E.: Hiding association rules by using confidence and support. In: Moskowitz, I.S. (ed.) IH 2001. LNCS, vol. 2137, pp. 369–383. Springer, Heidelberg (2001)
Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Fast algorithms for mining association rules in large databases. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–228 (2002)
Fournier-Viger, P., Gomariz, A., Gueniche, T., Soltani, A., Wu, C.W., Tseng, V.S.: SPMF: a Java Open-Source Pattern Mining Library. Journal of Machine Learning Research 15, 3389–3393 (2014)
Giannotti, F., Lakshmanan, L.V.S., Monreale, A., Pedreschi, D., Wang, H.W.: Privacy-preserving mining of association rules from outsourced transaction databases. IEEE Systems Journal 7(3), 385–395 (2012)
Goethals, B., Zaki, M.J.: Frequent itemset mining implementations repository (2012). http://fimi.ua.ac.be/data/
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Hong, T.P., Lin, C.W., Yang, K.T., Wang, S.L.: Using TF-IDF to hide sensitive itemsets. Applied Intelligence 38(4), 502–510 (2013)
Li, X.B., Sarkar, S.: A tree-based data perturbation approach for privacy-preserving data mining. IEEE Transactions on Knowledge and Data Engineering 18(9), 1278–1283 (2006)
Li, Y.C., Yeh, J.S., Chang, C.C.: MICF: An effective sanitization algorithm for hiding sensitive patterns on data mining. Advanced Engineering Informatics 21(3), 269–280 (2007)
Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: International Cryptology Conference on Advances in Cryptology, 36–54 (2000)
Liu, Y., Liao, W., Choudhary, A.K.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 689–695. Springer, Heidelberg (2005)
Lin, C.W., Hong, T.P., Lu, W.H.: An effective tree structure for mining high utility itemsets. Expert Systems with Applications 38(6), 7419–7424 (2011)
Lin, C.W., Zhang, B., Yang, K.T., Hong, T.P.: Efficiently hiding sensitive itemsets with transaction deletion based on genetic algorithms. The Scientific World Journal 2014, 1–13 (2014)
Lin, C.W., Hong, T.P., Wong, J.W., Lan, G.C., Lin, W.Y.: A GA-Based approach to hide sensitive high utility itemsets. The Scientific World Journal 2014, 1–12 (2014)
Sun, X., Yu, P.S.: A border-based approach for hiding sensitive frequent itemsets. In: IEEE International Conference on Data Mining, pp. 27–30 (2005)
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)
Verykios, V.S., Bertino, E., Fovino, I.N., Provenza, L.P., Saygin, Y., Theodoridis, Y.: State-of-the-art in privacy preserving data mining. ACM SIGMOD Record 33(1), 50–57 (2004)
Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: SIAM International Conference on Data Mining, pp. 482–486 (2004)
Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data and Knowledge Engineering 59(3), 603–626 (2006)
Yeh, J.S., Hsu, P.C.: HHUIF and MSICF: Novel algorithms for privacy preserving utility mining. Expert Systems with Applications 37(7), 4779–4786 (2010)
Yun, U., Kim, J.: A fast perturbation algorithm using tree structure for privacy preserving utility mining. Expert Systems with Applications 42(3), 1149–1165 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Lin, J.CW., Wu, TY., Fournier-Viger, P., Lin, G., Hong, TP., Pan, JS. (2016). A Sanitization Approach of Privacy Preserving Utility Mining. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. GEC 2015. Advances in Intelligent Systems and Computing, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-319-23207-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-23207-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23206-5
Online ISBN: 978-3-319-23207-2
eBook Packages: EngineeringEngineering (R0)