图书推荐服务用户隐私保护方法研究

User Privacy-preserving Method for Book Recommender Services

用户隐私安全问题正成为制约推荐服务发展应用的障碍.本文以图书推荐为例,构建实现了用户隐私安全的图书推荐服务,其基本思想是:在可信客户端精心伪造用户配置文件,以模糊用户敏感主题,改善用户隐私在不可信服务器端的安全性.首先,给出基于客户端的用户隐私安全的图书推荐框架,它不改变服务器端的推荐算法,也不影响推荐结果的准确性.然后,定义用户隐私模型,以形式化描述可信客户端伪造的配置文件应满足的约束条件.最后,借助于图书分类目录,给出用户隐私模型实现算法.理论分析和实验评估均验证了系统的有效性.

Recommender servicescan guide people to obtain target data from a tremendous amount of resources,and has become an important part of people’daily life.How ever,the privacy problem is becoming an important obstacle to the development and application of recommender.Using book recommender as an example,this paper designs and implements a user privacy-preserving book recommender system,w hose basic idea is to construct a group of fake profiles carefully for a user profile on a trusted client,to confuse users’sensitive topics,and thus improve the security of user privacy on the untrusted server.First,a client-based privacy-preserving book recommender framew ork is presented,w hich requires no change to the existing book recommender algorithm on the server,and no compromise to the book recommender accuracy.Then,a user privacy protection model is defined,w hich formally describes the constraints that the fake profiles constructed on the client should meet.Finally,w ith the help of the book classification catalogue,the implementation of user privacy model is presented.The effectiveness of the system is demonstrated by both theoretical analysis and experimental evaluation.