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Anonymization with MapReduce for Scalable BigData Privacy Preservation in Cloud

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Abstract
Data privacy is one of the most important concern issues when processing large datasets in Big Data Applications. Collection of enormous data-sets which make it difficult to process using on-hand database management tools or traditional data processing techniques is termed as Big Data. Big Data is characterized by 3 V’s, Volume, Value and Variety. Privacy to such huge datasets is a big problem which can be achieved by anonymization technique. Datasets like Electronics Health Records in such applications contain sensitive information, which brings about privacy issues especially, if the information is shared to public for data Analytics. The purpose of big data anonymization is to protect the privacy of the individual and only aggregate information is disclosed and makes it legal to share their data without getting permission from individuals. However, existing Privacy preservation techniques suffers from poor scalability and privacy disclosure risks. Hence a Map Reduced based k-mean clustering for data partitions and Divisive Hierarchical clustering to preserve anonymization until it satisfies the minimum privacy constraint is proposed. Our result shows that the utility of data is preserved for highly restricted privacy requirement and this approach significantly improves the scalability and efficiency over existing anonymization techniques.
Keywords:Bigdata, MapReduce, Hadoop, Data Anonymization, Privacy Preservation.

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