摘要： 针对协同过滤推荐系统在预测过程中容易泄漏用户概貌数据的问题，在不影响推荐准确性的前提下，提出一种用户数据混淆策略，使响应用户的评分数据在计算用户相似度之前被假数据代替，用户尽量少泄露(或不泄露)个人评分信息，进而实现用户隐私的保护。通过实验分析数据混淆策略对协同过滤推荐准确性的影响，证明该策略的有效性。

关键词: 协同过滤, 隐私保护, 推荐系统, 准确性

Abstract: On the basis of the users’ profileexposing in the prediction generation process, a data obfuscation policy without affecting the accuracy of recommender system is proposed, in which the ratings of the responding user are substituted by false data before computing the similarity degree of users. The process does not(or a little) expose the ratings of user and preserves users’ privacy data. Experimental results demonstrate the impact of obfuscation policies on the accuracy of the generated predictions, and show the improvement is effective.

Key words: collaborative filtering, privacypreserving, recommender system, accuracy

中图分类号: 

• TP309.2