面向群组推荐的个性化隐私保护方法

Personalized privacy protection method for group recommendation

为解决现有的隐私保护方法不能很好地满足群组推荐中用户的个性化隐私需求的问题,提出了一种面向群组推荐的基于可信客户端的个性化隐私保护框架及基于此框架的群组敏感偏好保护方法。所提方法在可信客户端收集群组内用户的历史数据以及隐私偏好需求,利用用户敏感主题相似性发现组内相似用户,通过对前k个用户进行随机的协同扰动,实现群组内用户的个性化隐私保护。仿真对比实验表明,所提的个性化隐私保护方法能够满足不同用户的隐私需求,具有更好的性能。

To address the problem that most of the existing privacy protection methods can not satisfy the user’s personalized requirements very well in group recommendation, a user personalized privacy protection framework based on trusted client for group recommendation(UPPPF-TC-GR) followed with a group sensitive preference protection method(GSPPM) was proposed. In GSPPM, user’s historical data and privacy preference demands were collected in the trusted client, and similar users were selected in the group based on sensitive topic similarity between users. Privacy protection for users who had privacy preferences in the group was realized by randomization of cooperative disturbance to top k similar users. Simulation experiments show that the proposed GSPPM can not only satisfy privacy protection requirements for each user but also achieve better performance.