Accuracy Constrained Top down Specialization Approach for Data Anonymization

Anonymizing data sets via generality to satisfy certain privacy requirements such as k-anonymity is a widely used category of privacy preserving techniques. Secrecy is one of the most disturbed issues in cloud computing. Personal data like financial operation records and automated health records are extremely sensitive although that can be analyzed and mined by organization. Data privacy issues need to be addressed urgently before data sets are shared on cloud. Data anonymization refers to as hiding complex data for owners of data records. Sharing the private data like economic transaction record in its most specific state poses a threat to individual privacy. Map Reduce algorithm for determining overview and provide protection for sensitive information. Data sets are generalized in a top-down manner until k-anonymity is violated, in order to expose the maximum effectiveness. In this paper, a scalable two- phase top-down specialization (TDS) approach to anonymize extensive data sets using the Map Reduce framework on cloud is to be proposed. In both phases of our approach, we deliberately design a group of innovative Map Reduce jobs to concretely accomplish the concentration computation in a highly scalable way.