A Review On Data Anonymization Technique For Data Publishing

A Review On Data Anonymization Technique For Data Publishing


Abstract: In recent years, for many kinds of structured data, including tabular, graph and item set data, data anonymization techniques have been subject of research. In this paper, we present brief yet systematic review of several anonymization techniques such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that generalization loses considerable amount of information, especially for high-dimensional data. On the other hand, bucketization does not prevent membership disclosure. Whereas slicing preserves better data utility than generalization and also prevents membership disclosure. This paper focus on effective method that can be used for providing better data utility and can handle high-dimensional data.