【摘要】 针对分布式数据存储结构的协同过滤推荐隐私保持问题,以可交换的密码系统为主要技术,设计了一个协议,集中解决其核心任务——在保持用户隐私前提下对项目评分.准确度与数据集中存放一样,但能保持各分站点下用户评分数据的隐私.基于安全多方计算理论和随机预言模型,证明了协议的安全性,分析了协议的时间复杂度和通信耗费.更多还原

【Abstract】 Privacy-preserving data mining is a cutting-edge research direction in recent years. As one of its sub-directions, privacy-preserving collaborative filtering aims at protecting users’ privacy while providing high-quality recommendations efficiently. To reserve privacy in collaborative filtering recommender systems under distributed data scenario, the core challenge——how to securely rate a specific item——is addressed. A protocol employing commutative encryption as its major privacy-preserving technique is introduced. This protocol produces the same results as the traditional memory-based collaborative filtering recommender systems while preventing any user’ ratings from being known by other sites rather than by itself. Based on secure multi-party computation and random oracle model, the protocol’s security is proved. The protocol’s computation complexity and communication costs are analyzed as well.更多还原