A MapReduce Based Approach of Scalable Multidimensional Anonymization for Big Data Privacy Preservation on Hadoop

Bigdata privacy preservation is one of the most disturbed issues on the current industry. 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. In this paper investigate the problem of big data anonymization for privacy preservation from the perspectives of scalability and time factor etc. At present, the scale of data in many cloud applications increases tremendously in accordance with the big data trend. Here propose a scalable Two Phase Top-Down Specialization (TPTDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud. For the data anonymization-45,222 records of adults information with 15 attribute values was taken as the input big data. With the help of multidimensional anonymization on map reducing framework, here implemented the proposed Two-Phase Top-Down Specialization anonymization algorithm on hadoop will increases the efficiency of the big data processing system. In both phases of the approach, deliberately design multidientional MapReduce jobs to concretely accomplish the specialization computation in a highly scalable way. Data sets are generalized in a top-down manner and the better result was shown in multidmientional MapReduce framework by compairing the onedimentional MapReduce framework anonymization job. The anonymization was performed with specialization operation on the taxonomy tree. The experiment demonstrates that the solutions can significantly improve the scalability and efficiency of big data privacy preservation compared to existing approaches. This work has great applications to both public and private sectors that share information to the society.