An Effective Specialization Approach for Data Anonymization Using Map Reduce on Cloud

Data privacy preservation is one of the most dispersed concerns on the modern business. Data sequestration concern urgency to be consigned crucially before data sets are communal on a cloud. Data anonymization refers to as concealing complicated data for heirs of data records. In this paper interrogate the complications of big data anonymization for privacy conservation from the context of scalability and time factor etc. At present, the extent of data in many cloud applications boosts exceedingly in accord with the big data trend. Here introduced a scalable Two Phase TopDown Specialization (TPTDS) accession to anonymize substantial data sets using the Map Reduce framework on a cloud. For the data anonymization, 45,222 records of adult’s report with 15 attribute aspect values were taken as the input big data. With the assistance of multifaceted anonymization on Map Reduce framework, here implement the proposed Two-Phase Top-Down Specialization anonymization algorithm on Hadoop will boost the productivity/competence of the big data processing system. In both aspect of the access, consciously study multidimensional Map Reduce jobs to categorically achieve the specialization computation in an extremely scalable way. Data sets are generalized in a top-down demeanor and the improved results shown in multidimensional Map Reduce framework by analysing the one-dimensional Map Reduce framework anonymization job. The anonymization was implemented with specialization procedure on the taxonomy tree. The analysis exhibit that the solutions can significantly advance the scalability and competence of big data privacy preservation compared to existing approaches. This task has enormous applications to both public and private sectors that stake data to the society. INTRODUCTION: Data secure concerned issues in cloud computing is very important to find the solution, and the concern aggravates in the background of cloud computing although some camouflage issues are not new. Personal data like electronic health reports and monetary transaction transcripts are usually presumed immensely sensitive although the above mentioned data can bid compelling human benefits if they are evaluated and mined by organizations such as health research centers. For instance, Microsoft HealthVault, an online cloud health service, accumulated data from users and stake the data with research institutes. Data confidentiality can be disclosed with less effort by malevolent cloud users or providers because of the failures of some traditional privacy protection measures consignment on the cloud. This can bring ample monetary loss or severe social prominence to data owners. Hence, data privacy issues need to be addressed urgently before data sets are analyzed or shared on the cloud. Data anonymization has been extensively studied and widely adopted for data privacy preservation in no bilateral data circulating and distribution scenarios. Data anonymization refers to hiding identity and/or sensitive data for holders of data records.