References
- Aggarwal, C. C., Pei, J., & Zhang, B. (2006). On privacy preservation against adversarial data mining. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 510–516.
- Agrawal, R., & Srikant, R. (1994a). Quest synthetic data generator. Retrieved from http://www.Almaden.ibm.com/cs/quest/syndata.html
- Agrawal, R., & Srikant, R. (1994b). Fast algorithms for mining association rules in large databases. The International Conference on Very Large Data Bases, Morgan Kaufmann Publishers, San Francisco, CA, USA, pp. 487–499.
- Agrawal, R., & Srikant, R. (2000). Privacy-preserving data mining. ACM SIGMOD Record, 29, 439–450.
- Atallah, M., Elmagarmid, A., Ibrahim, M., Bertino, E., & Verykios, V. (1999). Disclosure limitation of sensitive rule. The Workshop on Knowledge and Data Engineering Exchange, Chicago, IL, pp. 45–52.
- Bonam, J., Reddy, A. R., & Kalyani, G. (2014). Privacy preserving in association rule mining by data distortion using pso. Advances in Intelligent Systems and Computing, 249, 551–558.
- Chan, R., Yang, Q., & Shen, Y. D. (2003). Mining high utility itemsets. IEEE International Conference on Data Mining, Melbourne, FL, pp. 19–26.
- Cheng, P., Lin, C. W., & Pan, J. S. (2015). Use hype to hide association rules by adding items. PLOS One, 10(6), 1–19.
- Cheng, P., Roddick, J. F., Chu, S. C., & Lin, C. W. (2016). Privacy preservation through a greedy, distortion-based rule-hiding method. Applied Intelligence, 44, 295–306.
- Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002). Tools for privacy preserving distributed data mining. ACM SIGKDD Explorations Newsletter, 4, 28–34.
- Evfimievski, A., Srikant, R., Agrawal, R., & Gehrke, J. (2002). Privacy preserving mining of association rules. ACM International Conference on Knowledge Discovery and Data Mining, pp. 217–228.
- Fournier-Viger, P., Lin, J. C. W., Gomariz, A., Gueniche, T., Soltani, A., Deng, Z., & Lam, H. T. (2016). The spmf open-source data mining library version 2. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 36–40.
- Fournier-Viger, P., Lin, J. C. W., Kiran, R. U., Koh, Y. S., & Thomas, R. (2017). A survey of sequential pattern mining. Data Science and Pattern Recognition, 1, 54–77.
- Giannotti, F., Lakshmanan, L. V. S., Monreale, A., Pedreschi, D., & Wang, H. (2013). Privacy-preserving mining of association rules from outsourced transaction databases. IEEE Systems Journal, 7, 385–395.
- Han, J., Pei, J., Yin, Y., & Mao, R. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8, 53–87.
- Holland, J. H. (1992). Adaptation in natural and artificial systems. Cambridge, MA: MIT Press.
- Hong, T. P., & Wang, C. Y. (2006). Maintenance of association rules using pre-large itemsets. Intelligent Databases: Technologies and Applications, pp. 44–60.
- Hong, T. P., Lin, C. W., Yang, K. T., & Wang, S. L. (2013). Using tf-idf to hide sensitive itemsets. Applied Intelligence, 38, 502–510.
- Lin, C. W., Hong, T. P., & Lu, W. H. (2009). The pre-fufp algorithm for incremental mining. Expert Systems with Applications, 36, 9498–9505.
- Lin, C. W., Hong, T. P., & Lu, W. H. (2011). An effective tree structure for mining high utility itemsets. Expert Systems with Applications, 38, 7419–7424.
- Lin, C. W., Hong, T. P., Chang, C. C., & Wang, S. L. (2013). A greedy-based approach for hiding sensitive itemsets by transaction insertion. Journal of Information Hiding and Multimedia Signal Processing, 4, 201–214.
- Lin, C. W., Hong, T. P., Wong, J. W., Lan, G. C. & Lin, W. Y. (2014). Ga-based approach to hide sensitive high utility itemsets. Scientific World Journal, 12 p. Article ID 804629.
- Lin, J. C. W., Gan, W., Fournier-Viger, P., Hong, T. P., & Tseng, V. S. (2015). Efficient algorithms for mining high-utility itemsets in uncertain databases. Knowledge-based Systems, 96, 171–187.
- Lin, J. C. W., Wu, T. Y., Fournier-Viger, P., Lin, G., Hong, T. P., & Pan, J. S. (2015). A sanitization approach of privacy preserving utility mining. Advances in Intelligent Systems and Computing, 388, 47–57.
- Lin, J. C. W., Fournier-Viger, P., & Gan, W. (2016). Fhn: An efficient algorithm for mining high-utility itemsets with negative unit profits. Knowledge-Based Systems, 111, 283–298.
- Lin, J. C. W., Gan, W., Fournier-Viger, P., Hong, T. P., & Zhan, J. (2016). Efficient mining of high-utility itemsets using multiple minimum utility thresholds. Knowledge-Based Systems, 113, 100–115.
- Lindell, Y., & Pinkas, B. (2000). Privacy preserving data mining. The Annual International Cryptology Conference on Advances in Cryptology, Santa Barbara, CA, USA, pp. 36–54.
- Liu, J., Wang, K., & Fung, B. (2016). Mining high utility patterns in one phase without generating candidates. IEEE Transactions on Knowledge and Data Engineering, 28, 1245–1257.
- Liu, M., & Qu, J. (2012). Mining high utility itemsets without candidate generation. ACM International Conference on Information and Knowledge Management, pp. 55–64.
- Liu, Y., Liao, W. K., & Choudhary, A. (2005). A two-phase algorithm for fast discovery of high utility itemsets. Lecture Notes in Computer Science, 3518, 689–695.
- Oliveria, S. R. M., & Zaiane, O. R. (2002). Privacy preserving frequent itemset mining. IEEE International Conference on Privacy, Security and Data Mining, 14, 43–54.
- Rajalaxmi, R. R., & Nataraja, A. M. (2012). Effective sanitization approaches to hide sensitive utility and frequent itemsets. Intelligent Data Analysis, 16, 933–951.
- Verykios, V. S., Bertino, E., Fovino, I. N., Provenza, L. P., Saygin, Y., & Theodoridis, Y. (2004). State-of-the-art in privacy preserving data mining. ACM SIGMOD Record, 33, 50–57.
- Wu, Y. H., Chiang, C. M., & Chen, A. L. P. (2007). Hiding sensitive association rules with limited side effects. IEEE Transactions on Knowledge and Data Engineering, 19, 29–42.
- Yao, H., & Hamilton, H. J. (2006). Mining itemset utilities from transaction databases. Data & Knowledge Engineering, 59, 603–626.
- Yao, H., Hamilton, H. J., & Butz, C. J. (2004). A foundational approach to mining itemset utilities from databases. SIAM International Conference on Data Mining, Orlando, FL, pp. 482–486.
- Yeh, J. S., & Hsu, P. C. (2010). Hhuif and msicf: novel algorithms for privacy preserving utility mining. Expert Systems with Applications, 37, 4779–4786.
- Yun, U., & Kim, J. (2015). A fast perturbation algorithm using tree structure for privacy preserving utility mining. Expert Systems with Applications, 42, 1149–1165.