通信学报 ›› 2019, Vol. 40 ›› Issue (9): 106-115.doi: 10.11959/j.issn.1000-436x.2019183

• 学术论文 • 上一篇    下一篇

面向群组推荐的个性化隐私保护方法

王海艳1,2,陆金祥1   

  1. 1 南京邮电大学计算机学院,江苏 南京 210023
    2 南京邮电大学江苏省大数据安全与智能处理重点实验室,江苏 南京 210023
  • 修回日期:2019-07-04 出版日期:2019-09-25 发布日期:2019-09-28
  • 作者简介:王海艳(1974- ),女,江苏东台人,博士,南京邮电大学教授,主要研究方向为服务计算、可信计算、大数据应用与云计算技术、隐私保护技术等。|陆金祥(1993- ),男,江苏姜堰人,南京邮电大学硕士生,主要研究方向为推荐系统和隐私保护技术。
  • 基金资助:
    国家自然科学基金资助项目(61772285)

Personalized privacy protection method for group recommendation

Haiyan WANG1,2,Jinxiang LU1   

  1. 1 School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 Jiangsu Key Laboratory of Big Data Security &Intelligent Processing,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
  • Revised:2019-07-04 Online:2019-09-25 Published:2019-09-28
  • Supported by:
    The National Natural Science Foundation of China(61772285)

摘要:

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。

关键词: 群组推荐, 个性化隐私保护, 随机化扰动, k-匿名

Abstract:

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.

Key words: group recommendation, personalized privacy protection, randomized perturbation, k-anonymous

中图分类号: