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Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Mohamed Refaat Abdellah, H. Aboelseoud M., Khalid Shafee Badran, M. Badr Senousy
10.5120/ijca2016908446

Mohamed Refaat Abdellah, Aboelseoud H M., Khalid Shafee Badran and Badr M Senousy. Article: Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges. International Journal of Computer Applications 136(6):11-17, February 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Mohamed Refaat Abdellah and H. Aboelseoud M. and Khalid Shafee Badran and M. Badr Senousy},
	title = {Article: Privacy Preserving Association Rule Hiding Techniques: Current Research Challenges},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {136},
	number = {6},
	pages = {11-17},
	month = {February},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Association rule mining is one of the most used techniques of data mining that are utilized to extract the association rules from large databases. Association rules are one of the most significant assets of any organization that can be used for business growth and profitability increase. It contains sensitive information that threatens the privacy of its publication and it should be hidden before publishing the database. Privacy preserving data mining (PPDM) techniques is used to preserve such confidential information or restrictive patterns from unauthorized access. The pattern can be represented in the form of a frequent itemset or association rule. Also, a rule or pattern is marked as sensitive if its disclosure risk is above a given threshold. This paper discusses the current techniques and challenges of privacy preserving in association rule mining. Also, presentation of metrics used to evaluate the performance of those approaches is also given. Finally, Interesting future trends in this research body are specified.

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Keywords

Privacy preserving data mining, Association Rule, Hiding Approaches.