Novel Sensitive Information Preserving Mining (SIPM) algorithm for association rule mining in centralized database

The recent advancement in data mining technology to analyze vast amount of data has played an important role in several areas of Business processing. Data mining also opens new threats to privacy and information security if not done or used properly. The main problem is that from non-sensitive data, one is able to infer sensitive information, including personal information, fact or even patterns which are generated by any algorithm of data mining. In order to focusing on privacy preserving association rule mining, the simplistic solution to address the problem of privacy is presented. The solution is to survey different aspects which are discussed in the several research papers and after analyzing those research papers conclude a new solution which is best in efficiency and performance. In this paper we propose a novel algorithm named Sensitive Information Preserving Mining (SIPM). The entire system architecture consists of three phases: 1) Check for Authentication. 2) Reading the database. 3) Perform Pruning. Our algorithm is a good way to apply data mining techniques with security that hides our logical instances from others.