关键词: 数据发布, 隐私保护, l-多样性, 相似性攻击, 偏斜性攻击

Abstract: At present most [l]-diverse anonymity algorithms are vulnerable to similarity attack and skewness attack due to treating all sensitive attribute values equally and without considering the sensitivity and specific distribution. Moreover, these algorithms result in high information loss on account of performing full domain generalization to create equivalence class. This paper proposes a personalized [(l,c)]-anonymity algorithm based on clustering, which improves the security through defining sensitivity for different sensitive attribute value and maximal ratio threshold and reduces information loss via clustering technique. Theoretical analysis and experimental results indicate that the method is effective and feasible.

Key words: data release, privacy preservation, [l]-diversity, similarity attack, skewness attack