Review on Present State-of-the-Art of Secure and Privacy Preserving Data Mining Techniques

Abstract

As people of every walk of life are using Internet for various purposes there is growing evidence of proliferation of sensitive information. Security and privacy of data became an important concern. For this reason privacy preserving data mining (PPDM) has been an active research area. PPDM is a process discovering knowledge from voluminous data while protecting sensitive information. In this paper we explore the present state-of-the-art of secure and privacy preserving data mining algorithms or techniques which will help in real world usage of enterprise applications. The techniques discussed include randomized method, k-Anonymity, l-Diversity, t-Closeness, m-Privacy and other PPDM approaches. This paper also focuses on SQL injection attacks and prevention measures. The paper provides research insights into the areas of secure and privacy preserving data mining techniques or algorithms besides presenting gaps in the research that can be used to plan future research