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隐私保护数据挖掘 被引量：6

Privacy preserving data mining
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摘要 隐私保护数据挖掘的目标是寻找一种数据集变换方法,使得敏感数据或敏感知识在实施数据挖掘的过程中不被发现。近年出现了大量相关算法,按照隐私保持技术可将它们分为基于启发式技术、基于安全多方技术和基于重构技术三种。结合目前研究的热点对关联规则和分类规则的隐私保护数据挖掘进行介绍,并给出算法的评估方法,最后提出了关联规则隐私保护数据挖掘未来研究工作的方向。 The objective of privacy preserving data mining（PPDM） is to find a way to manipulate the dataset, so that the sensitive message can＇ t be disclosed in data mining. There are lots of algorithms proposed in recent years, they can be classified as heuristic-based techniques, seure multiparty techniques and reconstruction-based techniques by the privacy preserving technique. It gave an overview of PPDM in terms of the classification above, and presented some evaluation standards. Furthermore, it showed the future work of association rules hiding.
作者 李学明 刘志军 秦东霞
机构地区 重庆大学计算机学院
出处 《计算机应用研究》 CSCD 北大核心 2008年第12期3550-3555,共6页 Application Research of Computers
基金 重庆市自然科学基金资助项目(CSTC 2007BB2178)
关键词 数据挖掘 隐私保护 启发式技术 安全多方技术 重构技术 data mining privacy preserving heuristic technology seure multiparty technology reconstruction technology
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献33

  • 1VERYKIOS V S, BERTINO E, FOVINO I N, et al. State-of-the-art in privacy preserving data mining[ J]. SIGMOD Record, 2004,33 (1) :50-57.
  • 2JOHNSTEN T, RAGHAVAN V. A methodology for hiding knowledge in databases[ C ]//Proc of IEEE International Conference on Privacy, Security and Data Mining. Darlinghurst : Australian Computer Society, 2002:9-17.
  • 3ATALLAH M, ELMAGARMID A, LBRAHIM M, et al. Disclosure limitation of sensitive rules [ C ]//Proc of IEEE Workshop on Knowledge and Data Exchange. Washington DC : IEEE Computer Society, 1999:45-52.
  • 4CHEN X, ORLOWSKA M, LI X. A new framework for privacy preserving data sharing[ C ]//Proe of IEEE ICDM Workshop on Privacy and Security Aspects of Data Mining. Washington DC : IEEE Computer Society, 2004:47- 56.
  • 5KABIR S M A, YOUSSEF A M, ELHAKEEM A K. On data distortion for privacy preserving data mining[ C ]//Proc of Canadian Conference on Electrical and Computer Engineering. 2007:308- 311.
  • 6VERYKIOS V S, ELMAGARMID A, BERTINO E, et al. Association rule hiding[J]. IEEE Trans on Knowledge and Data Engineering, 2004,16 (4) :434-447.
  • 7OLIVEIRA S R M, ZAIANE O R. Algorithms for balancing privacy and knowledge discovery in association rule mining [ C ]//Proc of the 7th Int' 1 Database Engineering and Applications Symposium. Washington DC: IEEE Computer Society, 2003:54-63.
  • 8OLIVEIRA S R M, ZAIANE O R. Privacy preserving frequent itemset mining[ C ]//Proc of IEEE ICDM Workshop on Privacy, Security and Data Mining. Darlinghurst : Australian Computer Society, 2002 : 43- 54.
  • 9SUN Xing-zhi, YU P S. A border-based approach for hiding sensitive frequent itemsets [ C ]//Proc of the 5th IEEE Int'l Conference on Data Mining. Washington DC: IEEE Computer Society, 2005:426-433.
  • 10LEE Guan-ling, CHANG C Y, CHEN A L P. Hiding sensitive patterns in association rules mining[ C ]//Proc of the 28th Int'l Computer Software and Applications Conference. Washington DC: IEEE Computer Society, 2004:424-429.

二级参考文献6

  • 1Agrawal R,Srikant R.Privacy-preserving Data Mining[C].Proc.of the ACM SIGMOD Conference on Management of Data.ACM Press,2000-05:439-450.
  • 2Evmievski A,Srikant R,Agrawal R,et al.Privacy Preserving Mining of Association Rules[C].Proc.of 8th ACM SIGKDD Intl.Conf.on Knowledge Discovery and Data Mining,2002.
  • 3Rizvi S,Haritsa J R.Maintaining Data Privacy in Association Rule Mining[C].Proc.of 28th Int.Conf.on Very Large Databases,2002.
  • 4Du W,Zhan Z.Using Randomized Response Techniques for Privacy-preserving Data Mining[C].Proc.of 9th ACM SIGKDD Intl.Conf.on Knowledge Discovery and Data Mining,2003.
  • 5Agrawal R,Srikant R.Fast Algorithms for Mining Association Rules[C].Proc.of 20th Int.Conf.on Very Large Databases.Morgan Kaufmann,1994:487-499.
  • 6Synthetic Data Generation Code for Associations and Sequential Patterns[Z].http://www.almaden.ibm.com/software/quest/Resources/.

共引文献1

同被引文献75

引证文献6

二级引证文献20

计算机应用研究
2008年 第12期

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