面向群组推荐的个性化隐私保护方法

doi:10.11959/j.issn.1000-436x.2019183

摘要/Abstract

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:

TP393.0

王海艳,陆金祥. 面向群组推荐的个性化隐私保护方法[J]. 通信学报, 2019, 40(9): 106-115.

Haiyan WANG,Jinxiang LU. Personalized privacy protection method for group recommendation[J]. Journal on Communications, 2019, 40(9): 106-115.

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符号	描述	符号	描述
I	所有项目集合	G*	偏好项目集特征分布
U	所有用户集合	score(i)	用户对单个项目的评分
I (m)	第m个用户的项目集合	S*	u的偏好主题集合
r(u,i)	u对项目i的评分	r_i	群组内用户的评分向量
score(g)	u对主题g 的评分	$r_{i}^{*}$	隐私保护处理后的用户评分向量
sp	敏感项目	km	主题的最高层次
sus	相似用户的数组集合	P[]	扰动之后的用户偏好项目集
∩	集合中的元素评分间逻辑与运算	g*	群组内用户偏好主题集
SK*	偏好主题集特征分布	P*	群组内用户偏好项目集

群组大小		GSPPM			随机扰动方法
群组大小	性别划分	年龄划分	职业划分	性别划分	年龄划分	职业划分
1	0.997 54	0.984 51	0.983 56	0.998 51	0.985 56	0.985 47
20	0.950 17	0.940 25	0.938 87	0.968 19	0.950 12	0.937 95
30	0.938 63	0.938 71	0.926 84	0.941 02	0.940 19	0.929 76
40	0.934 09	0.930 19	0.921 16	0.940 01	0.939 17	0.924 87
50	0.935 08	0.924 54	0.920 61	0.937 86	0.932 97	0.922 16