BSGI: An Effective Algorithm towards Stronger l-Diversity

摘要

To reduce the risk of privacy disclosure during personal data publishing, the approach of anonymization is widely employed. On this topic, current studies mainly focus on two directions: (1)developing privacy preserving models which satisfy certain constraints, such as κ-anonymity, l-diversity, etc.; (2)de-signing algorithms for certain privacy preserving model to achieve better privacy protection as well as less information loss. This paper generally belongs to the second class. We introduce an effective algorithm "BSGI" for the widely accepted privacy preserving model: l-diversity. In the meantime, we propose a novel interpretation of l-diversity: Unique Distinct l-diversity, which can be properly achieved by BSGI. We substantiate it's a stronger l-diversity model than other interpretations. Related to the algorithm, we conduct the first research on the optimal assignment of parameter l according to certain dataset. Extensive experimental evaluation shows that Unique Distinct l-diversity provides much better protection than conventional l-diversity models, and BSGI greatly outperforms the state of the art in terms of both efficiency and data quality.