A Survey on Sanitizing Methods in Association Rule Hiding Technique

Abstract

In current years, the use of data mining techniques and related applications has enlarged a lot as it is used to extract important knowledge from large amount of data. Now a days the incredible growth of data in every field[1]. This increment of the data created lots of challenges in privacy. Privacy preserving in data mining becomes too essential due to share this data for our benefit purpose[2].This shared data may contain sensitive attributes, Database containing sensitive knowledge must be protected against illegal access. Therefore this it has become necessary to hide sensitive knowledge in database. Privacy preserving data mining (PPDM) try to conquer this problem by protecting the privacy of data without sacrificing the integrity of data. A number of techniques have been proposed for privacy-preserving data mining. To address this problem, Privacy Preservation Data Mining (PPDM) include association rule hiding method to protect privacy of sensitive data against association rule mining. In this paper, we survey different existing approaches to association rule hiding, along with some open challenges. We have also summarized few of the recent evolution. and a review of different techniques for privacy preserving data mining along with merits and demerits.