关键词: 隐私保护, 微聚集, k-匿名, l-多样性

Abstract: Traditional k-anonymity methods cannot resist homogeneity attack and background knowledge attack. In order to solve this problem, this paper proposes a sensitive attribute diversity micro-aggregation algorithm, the algorithm groups l nearest tuples to cluster center into one group, which has l different sensitive values. It extends this cluster based on satisfying the l-diversity, so the anonymous table l-diversity yield by the algorithm satisfies sensitive attribute l-diversity can resist homogeneity attack and background knowledge attack. Experimental results show that the algorithm can generate anonymity table to satisfy the needs of sensitive attribute diversity, and to ensure the availability of anonymous table.

Key words: privacy protection, microaggregation, k-anonymization, l-diversity