Density Clustering Based l-Diversity Data Publishing

Provided by: Binary Information Press
Topic: Data Management
Format: PDF
In recent years, sensitive information disclosure has become a serious concern in privacy preserving data publication because of the wide availability of personal data. A novel l-diversity privacy model was proposed due to the flaw of k-anonymity. However, the existing methods to achieve l-diversity based on generalization that lead to superabundant information loss and some researcher has proven that it is a NP-hardness problem. To minimize the information loss due to l-diversity, it is crucial to group similar data together and then to publish each cluster individually.

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