Privacy-Preserving in Data Mining using Anonymity Algorithm for Relational Data

Data mining is the process of analyzing data from different perspectives. To summarize it into useful information, we can consider several algorithms. To protect data from unauthorized user in this case is a problem to solve. Access control mechanisms protect sensitive information from unauthorized users. But if the privacy protected information is not in proper format, again the user will compromise the privacy and quality of data. A privacy protection mechanism can use suppression and generalization of relational data to anonymize and satisfy privacy requirements, e. g. , k-anonymity and l-diversity, against identity and attribute disclosure. However, privacy is achieved at the cost of precision of authorized information. In this paper, we propose an accuracy-constrained privacy-preserving access control framework. The access control policies define selection predicates available to roles while the privacy requirement is to satisfy the k-anonymity or l-diversity. An additional constraint that needs to be satisfied by the PPM is the imprecision bound for each selection predicate. The techniques for workload-aware anonymization for selection predicates have been discussed in the literature. However, to the best of our knowledge, the problem of satisfying the accuracy constraints for multiple roles has not been studied before. In our formulation of the aforementioned problem, we propose heuristics for anonymization algorithms and show empirically that the proposed approach satisfies imprecision bounds for more permissions and has lower total imprecision than the current state of the art.