摘要： MDAV算法是一种高效的微聚集算法，但它未考虑等价类中敏感属性多样性问题，生成的匿名表不能抵制同质性攻击和背景知识攻击。针对该问题，提出一种能够实现(k,e)-匿名模型的MDAV算法，简称(k,e)-MDAV算法。该算法将距离类中心最近的至少k个不同敏感值的元组聚为一类，并要求每个类内敏感属性值最大差异至少为e。实验结果表明，该算法能够生成满足(k,e)-匿名模型的匿名表。

关键词: (k, e)-匿名, k-匿名, 微聚集, 同质性攻击, 背景知识攻击

Abstract: MDAV(Maximum Distance to Average Vector) algorithm is an efficient microaggregation algorithm. However, it does not capture diversity of sensitive values in each equivalence class, so the anonymity table generated by the algorithm cannot resist homogeneity attack and background knowledge attack. To solve the problem, the paper proposes a (k,e)-MDAV algorithm. The algorithm groups at least the k nearest tuples to cluster center into one cluster, and further requires the range of the distinct values in one cluster to be no less than e. Experimental results show the algorithm can generate anonymity table satisfying (k,e) anonymity model efficiently.

Key words: (k,e)-anonymity, k-anonymity, microaggregation, homogeneity attack, background knowledge attack

中图分类号: 

• TP311.52