一种带隐私保护的基于标签的推荐算法研究

摘要

在基于标签的推荐中,标签起着联系用户和信息资源的作用.但由于存在语义特性,相较于评分数据,标签数据在一定程度上更能够直接反映用户喜好,隐私问题更为突出.推荐服务器收集用户的历史标签记录,一旦攻击者通过攻击推荐服务器而获得了用户信息,将造成严重的用户隐私泄露问题.对此,提出一种带有隐私保护的基于标签k-means聚类的资源推荐方法CDP k-meansRA,即利用Crowds网络进行用户发送方匿名保护,并且将ε-差分隐私保护融入改进的标签k-means聚类算法中.通过实验将提出的CDP k-meansRA与k-meansRA等算法进行比较,证明了CDP k-meansRA能够在保护用户隐私的前提下,保证一定的推荐质量.%In tag-based recommendation,tags play a role in the link between users and information resources.However,compared to rating data,since the semantic properties of the tag data,tag data reflects user preferences more directly,so the privacy issues in tag-based recommendation are more serious.Recommender server collects user history tag records,once an attacker accesses the user information by attacking the recommender server,it will cause serious leakage of user privacy.A resource recommendation method (CDP k-meansRA) based on tag k-means clustering with privacy protection is proposed.Sender anonymity protection is provided by using Crowds network and ε-differential privacy are fused into an improved tag clustering based recommendation algorithm.The experiments show that compared to k-meansRA and so on,the CDP k-meansRA can keep the quality of the recommendation under the premise of user privacy preservation.