Adaptive Utility-based Anonymization Model: Performance Evaluation on Big Data Sets

Abstract Data Anonymization is one of the globally accepted mechanisms for the protection of privacy of individuals in data publishing scenario. Normally the data anonymization impacts on the quality of data especially critical to the success of knowledge-based applications. An intelligent approach based on association mining namely, Adaptive Utility-based Anonymization (AUA) has been proposed in order to deal with this issue. Initially the model is tested with sample instances of original data set National Family Health Survey (NFHS-3) and this paper includes performance evaluation of AUA model using data sets and proves that the data anonymization can be done without compromising the quality of data mining results.

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