The week's pick
Reseach Article
Approaches for Privacy Preserving Data Mining by Various Associations Rule Hiding Algorithms – A Survey
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 11 |
| Year of Publication: 2016 |
| Authors: Umesh Kumar Sahu, Anju Singh |
10.5120/ijca2016908042
|
Umesh Kumar Sahu, Anju Singh . Approaches for Privacy Preserving Data Mining by Various Associations Rule Hiding Algorithms – A Survey. International Journal of Computer Applications. 134, 11 ( January 2016), 21-26. DOI=10.5120/ijca2016908042
Abstract
Yesteryears, data mining has emerged as a very popular tool for extracting hidden knowledge from collection of huge amount of data. Major challenges of data mining are to find the hidden knowledge in the data while the sensitive information is not revealed. Many Industry ,Defence ,Public Sector and Organisation facing risk or having security issue while sharing their data so it is very crucial concern how to protect their sensitive information due to legal and customer concern. Many strategies have been proposed to hide the information containing sensitive data. Privacy preserving data mining is an answer to such problems. Association rule hiding is one of the PPDM techniques to protect the sensitive association rule .In this paper, all the approaches for privacy preserving data mining have been compared theoretically and points out their pros and cons.
References
- Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes Gehrke. Privacy Preserving Mining of Association Rules. SIGKDD 2002, Edmonton, Alberta Canada.
- D. Agrawal and C. C. Aggarwal, "On the design and quantification of privacy preserving data mining algorithms", In Proceedings of the 20th Symposium on Principles of Database Systems, Santa Barbara, California, USA, May, 2001.
- Stanley R. M. Oliveira and Osmar R. Zaiane, “Privacy preserving frequent itemset mining, InProceedings of the IEEE ICDM Workshop on Privacy, Security and Data Mining (2002), pp.43–54.
- S.R.M. Oliveira, O.R. Zaıane, Y. Saygin, “Secure association rule sharing, advances in knowledge discovery and data mining, in: Proceedings of the 8th Pacific-Asia Conference (PAKDD2004), Sydney, Australia, 2004, pp.74–85.
- E. Dasseni, V. Verykios, A. Elmagarmid & E. Bertino, “Hiding association rules by using confidence and support” In Proceedings of 4th information hiding workshop, Pittsburgh,2001.
- Khyati B. Jadav, Jignesh Vania, Dhiren R. Patel “A Survey on Association Rule Hiding Methods” International Journal of Computer Applications, November 2013.
- D. Agrawal and C. C. Aggarwal, "On the design and quantification of privacy preserving data mining algorithms", In Proceedings of the 20th Symposium on Principles of Database Systems, Santa Barbara, California, USA, May, 2001.
- Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Johannes Gehrke. Privacy Preserving Mining of Association Rules. SIGKDD 2002, Edmonton, Alberta Canada.
- Yucel Saygin, Vassilios S. Verykios, and Ahmed K. Elmagarmid, “Privacy preserving association rule mining,” In Proceedings of the 12th International Workshop on Research Issues in Data Engineering (2002), 151–158.
- S. Oliveira & O. Zaiane, “Algorithms for balancing privacy and knowledge discovery in association rule mining” In Proceedings of 7th international database engineering and applications symposium (IDEAS03), Hong Kong, July 2003.
- Shyue-Liang Wang, Bhavesh Parikh, Ayat Jafari “Hiding informative association rule sets”, ELSEVIER, Expert Systems with Applications 2007.
- Rakesh Agrawal and Ramakrishnan Srikant, “Privacy-preserving data mining,” In Proceedings of the ACM SIGMOD Conference on Management of Data (2000), pp.439–450.
- Chen, X., Orlowska, M., and Li, X., "A new framework for privacy preserving data sharing.", In: Proc. of the 4th IEEE ICDM Workshop: Privacy and Security Aspects of Data Mining. IEEE Computer Society, 2004. 47-56.
- Yongcheng Luo, Yan Zhao, Jiajin Le, "A Survey on the Privacy Preserving Algorithm of Association Rule Mining", isecs, vol.1, pp.241-245, 2009
- Chris Clifton, Murat Kantarcioglou, XiadongLin and Michaed Y.Zhu, “Tools for privacy preserving distributed data mining,” SIGKDD Explorations 4, no. 2, 2002
- Ioannidis, I.; Grama, A, Atallah, M., “A secure protocol for computing dot-products in clustered and distributed environments,” Proceedings of International Conference on Parallel Processing, 18-21 Aug. 2002, pp.379–384.
- Shaofei Wu and Hui Wang ,"Research On The PrivacyPreserving Algorithm Of Association Rule Mining InCentralized Database”, IEEE International Symposiums on Information Processing, 2008.
- Chirag N. Modi, Udai Pratap Rao and Dhiren R. Patel, "An Efficient Approach for Preventing disclosure of Sensitive Association Rules in Databases", International Conference on Advances in Communication, Network, and Computing,IEEE, 2010
- Gkoulalas-Divanis and V.S.Verykios, “An Integer Programming Approach for Frequent Itemset Hiding”, In Proc. ACM Conf. Information and Knowledge Management (CIKM’06), Nov. 2006
- Gkoulalas-Divanis and V.S. Verykios, “Exact Knowledge Hiding through Database Extension,” IEEE Transactions on Knowledge and Data Engineering, vol. 21(5), May 2009, pp. 699-713.
- Gkoulalas-Divanis, Aris, Verykios, Vassilios S. “Association Rule Hiding for Data Mining”,Springer Series: Advances in Database Systems, Vol. 41, 1st Edition., 2010, p.13.
- C. Clifton, “Protecting against data mining through samples” In Proceedings of the thirteenth annual IFIP WG 11.3 working conference on database security, 1999.
- R. Agrawal & R. Srikant, “Privacy preserving data mining” In ACM SIGMOD conference on management of data, Dallas, Texas, May 2000
- C. Clifton, “Using sample size to limit exposure todata mining” Journal of Computer Security, 2000.
- Komal Shah, Amit Thakkar, Amit Ganatra,” A Study on Association Rule Hiding Approaches” International Journal of Engineering and Advanced Technology (IJEAT), February 2012.
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