基于个性化k匿名隐私保护的资源推荐算法

Resource RecommendationAlgorithm Based on K-anonymity for Generalizing User Query Requests

Existing resource recommendation algorithms make resource recommendation based on user preferences, without consider- ing the subjective reluctance of users to personal privacy or the data mining of interests and hobbies, or the risk of privacy disclosure of third-party servers. To solve the problem of user privacy, this paper puts forward the K-anonymous privacy protection algorithm. After the generalization of sensitive attributes of user query requests, it constructs a logical anonymous query request equivalence class, uses the method of data rotary to enable users in the same equivalence class to randomly forward the received data from other users. Because the privacy attributes that each person wants to protect are different, the weight of its sensitive attribute is different, so this paper proposes the weight summation formula based on the sensitive attribute combined with the sensitive attribute weight value set by the user independently, and recommends the optimal selection scheme for the platform user. The security analysis shows that this method can effectively resist similarity attack, background knowledge attack and capture server attack. Experiments show that this method not only satisfies the correctness of matching results, but also enhances the privacy protection performance in the process of resource recommendation.