融合微聚集隐私保护的协同过滤算法研究

Research on microaggregation fused collaborative filtering algorithm for privacy protection

现有的k-匿名隐私保护是一种安全有效的隐私保护算法,针对其对背景知识攻击和同质性攻击防范的不足,提出一种基于敏感属性多样性的微聚集隐私保护的协同过滤算法。算法在满足k-匿名的前提下,融入敏感属性的多样性,在微聚集算法中通过设置同一等价类中敏感属性的差异值,来避免敏感属性值过于接近而造成隐私泄露,从而达到保护隐私数据的目的,同时保证推荐的准确性。实验结果表明,该算法既能保证为用户提供高效的个性化推荐,又能够产生安全的信息表。

As a safe and effective privacy protection algorithm,the existing k-anonymous privacy protection still has some insufficiency caused by background knowledge attack and homogeneity attack. Therefore,a sensitive attribute diversity based microaggregation collaborative filtering algorithm for privacy protection is proposed. On the premise of meeting the k-anonymity requirement,the sensitive attribute diversity is fused into the algorithm. The difference values of sensitive attributes in the same equivalence class are set in the microaggregation algorithm to avoid too close sensitive attribute values which can cause privacy disclosure,so as to achieve the purpose of protecting the privacy data and ensure the accuracy of recommendation. The experimental results show that the algorithm can not only guarantee to provide users with efficient personalized recommendation,but also generate safe information tables.