Framework to Reduce the Hiding Failure Due to Randomized Additive Data Modification PPDM Technique
The development of technology in different domains facilitated organizations to collect huge amount of data for analysis purpose. Such data is analyzed by data mining tools to find trends, relationships and outcomes to enhance business activities and discover new patterns that may allow them to serve their customers and society in a better way. The data collected from various sources also consists of sensitive information. The application of data mining techniques on such databases may violate the privacy of an individual by revealing the information which is private and confidential. The threat of privacy due to data mining results has triggered development of many privacy-preserving data mining techniques.. In this paper we discussed the negative side of randomized additive perturbation technique and proposed a framework SARP which protects privacy by integrating basic data modification techniques. The experimental results shows that better privacy protection can be achieved with this framework than randomized additive perturbation technique.
KeywordsData mining Privacy Privacy preserving data mining swapping
Unable to display preview. Download preview PDF.
- 1.Agrawal, D., Aggawal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the 20th ACM SIMOD Symposium on Principles of Database Systems, Santa Barbara, pp. 247–255 (2001)Google Scholar
- 2.Agrawal, R., Srikant, R.: Privacy-preserving data mining. In: Proceeding of the ACM SIGMOD Conference on Management of Data, pp. 439–450. ACM Press, Dallas (2000)Google Scholar
- 3.Muralidhar, K., Sarathi, R.: A General additive data perturbation method for data base security. Journal of Management Science, 1399–1415 (2002)Google Scholar
- 4.Agrawal, D., Aggarwal, C.C.: On the Design and Quantification of Privacy Preserving Data mining algorithms. In: ACM PODS Conference (2002)Google Scholar
- 5.Canny, J.: Collaborative filtering with privacy. In: IEEE Symposium on Security and Privacy, Oakland, pp. 45–57 Google Scholar
- 7.Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the Privacy Preserving Properties of Random Data Perturbation Techniques. In: Proceedings of the 3rd International Conference on Data Mining, pp. 99–106 (2003)Google Scholar
- 9.Liu, L., Thuraisingham, B., Kantarcioglu, M., Khan, L.: An Adaptable Perturbation Model of Privacy Preserving Data Mining. In: ICDM Workshop on Privacy and Security Aspects of Data Mining, Huston, TX US (2005)Google Scholar
- 10.Fienberg, S.: Privacy and Confidentiality in an e-Commerce World. In: Data Mining, DataWarehousing, Matching and Disclosure Limitation. Statistical Science, 143–154 (2006) Google Scholar
- 11.Souptik, D., Kargupta, H., Sivakumar, K.: Homeland Defense, Privacy-Sensitive Data Mining, and Random Value Distortion. In: SIAM Workshop on Data Mining for Counter Terrorism and Security (2003)Google Scholar
- 13.Chen, K., Liu, L.: A random rotation perturbation approach to privacy preserving data classification. In: International conference on Data Mining, ICDM (2005)Google Scholar