A Survey on Heuristic Based Approach for Privacy Preserving in Data Mining

Abstract

Data Mining has been the most researched area for researchers because of the possibilities of extension at each application of it. When the data becomes massive in volume, many problems strike for security and privacy breach. Some applications like sharing of such data to a particular user have threats of preserving the original data so that the injection of such data can be prohibited. So it is a timely need to secure the data while handling them to the known or unknown users. The requirement of not losing the essence of data and still publishing it with the actual information is a challenge. Such troubles prompted the advancement of Privacy Preserving Data Mining (PPDM) Techniques. Privacy Preserving has become an important issue in the development progress of Data Mining techniques. Methods like k-Anonymity, l-Diversity have been explored well by researchers but still, there are holes that force us to develop a more effective method and using such approach one can get better accuracy with minimum loss of data.

Certain Inverstigations on Privacy-Preserving Data Mining Using Three-Dimensional Spin Revolution (3DSR) Scheme

“…Ananthi Sheshasaayee and Logeshwari portrayed that the present day's advancement of trading promotion is enhanced based on the user classification designs [5]. Patel et al portrayed that the information extraction is an interesting field due to its wide variety applications [6]. Therefore, there is no confidentiality security policy that overcomes all the prevailing conditions.…”