Privacy Preserving Data Mining Techniques: Current Scenario and Future Prospects

Abstract

Privacy preserving has originated as an important concern with reference to the success of the data mining. Privacy preserving data mining (PPDM) deals with protecting the privacy of individual data or sensitive knowledge without sacrificing the utility of the data. People have become well aware of the privacy intrusions on their personal data and are very reluctant to share their sensitive information. This may lead to the inadvertent results of the data mining. Within the constraints of privacy, several methods have been proposed but still this branch of research is in its infancy. The success of privacy preserving data mining algorithms is measured in terms of its performance, data utility, level of uncertainty or resistance to data mining algorithms etc. However no privacy preserving algorithm exists that outperforms all others on all possible criteria. Rather, an algorithm may perform better than another on one specific criterion. So, the aim of this paper is to present current scenario of privacy preserving data mining tools and techniques and propose some future research directions.