Towards a Differentially Private Data Anonymization

Maximizing data usage and minimizing privacy risk are two conflicting goals. Organizations always hide the owners' identities and then apply a set of transformations on their data before releasing it. While determining the best set of transformations has been the focus of extensive work in the database community, most of this work suffered from one or two of the following major problems: scalability and privacy guarantee. To the best of the authors' knowledge, none of the proposed scalable anonymization techniques provides privacy guarantees supported with well-formulated theoretical foundation.

Provided by: Purdue University Topic: Security Date Added: Jan 2012 Format: PDF

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