Multiobjective-integer-programming-based Sensitive Frequent Itemsets Hiding

详细信息    查看全文

• 作者：Mingzheng Wang (20)
  Yue He (20)
  Donghua Pan (20)
  
• 关键词：privacy preserving data mining ; association rule ; sensitive knowledge protection ; multi ; objective integer programming
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：8041
• 期：1
• 页码：336-348
• 全文大小：250KB
• 参考文献：1. Menon, S., Sarkar, S., Mukherjee, S.: Maximizing accuracy of shared databases when concealing sensitive patterns. Information Systems Research?16(3), 256-70 (2005) CrossRef
  2. Oliveira, S.R.M., Za?ane, O.R.: Privacy preserving frequent itemset mining. In: Proc. of the 2002 IEEE International Conference on Privacy, Security and Data Mining (CRPIT 2002), pp. 43-4 (2002)
  3. Oliveira, S.R.M., Za?ane, O.R.: Protecting sensitive knowledge by data sanitization. In: Proceeding of the Third IEEE International Conference on Data Mining (ICDM 2003), pp. 211-18 (2003)
  4. Amiri, A.: Dare to share:Protecting sensitive knowledge with data sanitization. Decision Support Systems?43(1), 181-91 (2007) CrossRef
  5. Sun, X., Yu, P.S.: A border-based approach for hiding sensitive frequent itemsets. In: ICDM 2005: Proceedings of the Fifth IEEE International Conference on Data Mining, pp. 426-33 (2005)
  6. Menon, S., Sarkar, S.: Minimizing information loss and preserving privacy. Management Science?53(1), 101-16 (2007) CrossRef
  7. Guo, Y.: Reconstruction-Based Association Rule Hiding. In: Proceedings of SIGMOD 2007 Ph.D.Workshop on Innovative Database Research 2007 (IDAR 2007), June 10 (2007)
  8. Gkoulalas-Divanis, A., Verykios, V.S.: Exact Knowledge Hiding through Database Extension. IEEE Transactions on Knowlege and Data Engineering?21(5), 699-13 (2009) CrossRef
  
• 作者单位：Mingzheng Wang (20)
  Yue He (20)
  Donghua Pan (20)
  
  20. No.2, Linggong Road, Ganjingzi District, Dalian, 116024, P.R. China
  

文摘

Due to substantial commercial benefits in the discovered frequent patterns from large databases, frequent itemsets mining has become one of the most meaningful studies in data mining. However, it also increases the risk of disclosing some sensitive patterns through the data mining process. In this paper, a multi-objective integer programming, considering both data accuracy and information loss, is proposed to solve the problem for hiding sensitive frequent itemsets. Further, we solve this optimization model by a two-phased procedure, where in the first procedure the sanitized transactions can be pinpointed and in the second procedure the sanitized items can be pinpointed. Finally, we conduct some extensive tests on publicly available real data. These experiments-results illustrate that our approach is very effective.