Secure Data Protection Using Slicing as a Confusion Technique

Abstract

Data Mining deals with automatic extraction of previously unknown patterns from large amounts of data sets. These data sets typically contain sensitive individual information or critical business information, which consequently get exposed to the other parties during Data Mining activities. Secure data protection has been one of the greater concerns in data mining. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy protective microdata publishing. The generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. Solution to this problem is provided by we introduce a novel data anonymization technique called slicing to improve the current state of the art.