Hiding Sensitive Association Rules by Sanitizing

Hui Wang

doi:10.4028/www.scientific.net/AMR.694-697.2317

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697Hiding Sensitive Association Rules by Sanitizing

Hiding Sensitive Association Rules by Sanitizing

Abstract:

The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.

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Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

2317-2321

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.2317

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Online since:

May 2013

Authors:

Hui Wang

Keywords:

Association Rule Hiding, Data Mining (DM), Privacy Preserving, Sanitizing, Sensitive Rule

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References

[1] R Agrawal and R Srikant, "Privacy preserving data mining", SIGMOD Record, 29(2), 2000, pp.439-450.

DOI: 10.1145/335191.335438

Google Scholar

[2] V.S. Verykios and A.Gkoulalas-Divanis, "A survey of association rule hiding methods for privacy", in Privacy Preserving Data Mining: Models and Algorithms, Springer Berlin Heidelberg, 2008, pp.267-289.

DOI: 10.1007/978-0-387-70992-5_11

Google Scholar

[3] A Gkoulalas-Divanis and VS Verykios, "Privacy preserving data mining: how far can we go?", in Handbook of Research on Data Mining in Public and Private Sectors: Organizational and Governmental Applications, IGI Global, 2009, pp.1-21.

DOI: 10.4018/978-1-60566-906-9.ch007

Google Scholar

[4] D.Jain, P.Khatri, R.Soni and B.K. Chaurasia, "Hiding sensitive association rules without altering the support of sensitive item(s)", in Advances in Computer Science and Information Technology, Networks and Communications, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Volume 84, 2012, pp.500-509.

DOI: 10.1007/978-3-642-27299-8_52

Google Scholar

[5] Yogendra Kumar Jain, Vinod Kumar Yadav and Geetika S. Panday, "An efficient association rule hiding algorithm for privacy preserving data mining", International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 7, July 2011, pp.2792-2798.

Google Scholar

[6] A.Gkoulalas-Divanis and V.S. Verykios, "Association Rule Hiding for Data Mining", Advances in Database Systems 41, Springer Science Business Media, LLC 2010.

DOI: 10.1007/978-1-4419-6569-1

Google Scholar