Hiding Sensitive Association Rules by Adjusting Support

Abstract:

Data mining technologies are successfully applied in lots of domains such as business, science research, health care, bioinformatics, financial forecasting and so on and so forth. Knowledge can be discovered by data mining and can help people to make better decisions and benefits. Association rule is one kind of the most popular knowledge discovered by data mining. While at the same time, some association rules extracted from data mining can be considered so sensitive for data holders that they will not like to share and really want to hide. Such kind of side effects of data mining is analyzed by privacy preserving technologies. In this work, we have proposed strategies by adjusting supports and quality measurements of sensitive association rules hiding.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 1875-1878
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.1875
• Online since: September 2013
• Authors: Hui Wang
• Keywords: Association Rule Hiding, Data Mining (DM), Privacy Preserving, Quality Measurements, Sensitive Rule

References

[1] 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

[2] A Gkoulalas-Divanis and VS 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

[3] Dhyanendra Jain, Pallavi Khatri, Rishi Soni and Brijesh Kumar 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

[4] DE O'Leary, Knowledge discovery as a threat to database security, in G. Piatetsky-Shapiro and W.J. Frawley WJ (eds. ) Knowledge Discovery in Databases, Menlo Park: AAAI Press; Cambridge: MIT Press, 1991, pp.507-516.

[5] C Clifton and D Marks, Security and privacy implications of data mining, in JW Han, LVS Lakshmanan and R Ng (eds. ), Proc. of the ACM SIGMOD Workshop Data Mining and Knowledge Discovery, Vancouver: University of British Columbia, 1996, pp.15-19.

[6] M Atallah, E Bertino, A Elmagarmid, M Ibrahim and VS Verykios, Disclosure limitation of sensitive rules", in P Scheuermann (ed. ) Proc. of the IEEE Knowledge and Data Exchange Workshop (KDEX, 99), Chicago: IEEE Computer Society, 1999, pp.45-52.

DOI: 10.1109/kdex.1999.836532