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A Survey on Data Anonymization Approaches Using MapReduce Framework on Cloud
Abstract
Many Cloud users require exchanging the Private data over internet for Data analysis and mining. Like Sensitive data require to Health Research centre for analysis purpose. Private data require high Privacy and this privacy preserved by some Privacy preservation techniques. Anonymization is one of the most important privacy preservation techniques. Data anonymization has several approaches like Top down, Bottom up. Both approaches executed until K-anonymity violated. In this paper we compare the performance of both approaches according K-anonymity. For parallel processing of large scale data on Cloud we use the MapReduce framework. MapReduce increase the computation power of Cloud. Anonymization approaches performs efficiently according to K-anonymity. From last several years scalability and efficiency problems occurs in data anonymization. There is several challenges for anonymization that is Scalability and Efficiency, another is rapidly growing scale of data on cloud that is Big data.
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References
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