面向群组推荐的个性化隐私保护方法

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

Abstract

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

CLC Number:

TP393.0

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

Figures/Tables 9

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

References 30

[1]	SWEENEY L . k-anonymity:a model for protecting privacy[J]. International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems, 2002,10(5): 557-570.
[2]	KAPLAN E , GURSOY M E , NERGIZ M E ,et al. Known sample attacks on relation preserving data transformations[J]. IEEE Transactions on Dependable and Secure Computing, 2017,1(32): 101-108.
[3]	AHMED K W , MOURI I J , ZAMAN R ,et al. A privacy preserving personalized group recommendation framework[C]// International Conference on Advanced Computing. IEEE, 2016: 594-598.
[4]	LU Z , SHEN H . A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm[C]// International Database Engineering and Applications Symposium. ACM, 2015: 72-80.
[5]	LI C , PALANISAMY B , JOSHI J . SocialMix:supporting privacy-aware trusted social networking services[C]// IEEE International Conference on Web Services. IEEE, 2016: 115-122.
[6]	LIU P , LI Y , SANG Y ,et al. Anonymity-based privacy preserving network data publication[C]// Trustcom/BigDataSE/ISPA. IEEE, 2017: 823-828.
[7]	TANG Q , WANG J . Privacy-preserving friendship-based recommender systems[J]. IEEE Transactions on Dependable and Secure Computing, 2018,15(5): 784-796.
[8]	AHMADI M , GHAHFAROKHI B S . Preserving privacy in location based mobile coupon systems using anonymous authentication scheme[C]// International Iranian Society of Cryptology Conference on Information Security and Cryptology. IEEE, 2016: 60-65.
[9]	GUO L , ZHANG C , FANG Y . A trust-based privacy-preserving friend recommendation scheme for online social networks[J]. IEEE Transactions on Dependable ＆ Secure Computing, 2015,12(4): 413-427.
[10]	PENG T , LIU Q , WANG G . Enhanced location privacy preserving scheme in location-based services[J]. IEEE Systems Journal, 2014,11(1): 219-230.
[11]	ZHU J , HE P , ZHENG Z ,et al. A privacy-preserving QoS prediction framework for Web service recommendation[C]// IEEE International Conference on Web Services. IEEE, 2015: 241-248.
[12]	GAI K , QIU M , ZHAO H ,et al. Privacy-aware adaptive data encryption strategy of big data in cloud computing[C]// IEEE International Conference on Cyber Security and Cloud Computing. IEEE, 2016: 273-278.
[13]	何明, 常盟盟, 吴小飞 . 一种基于差分隐私保护的协同过滤推荐方法[J]. 计算机研究与发展, 2017,54(7): 1439-1451.
	HE M , CHANG M M , WU X F . A collaborative filtering recommen-dation method based on differential privacy protection[J]. Computer Research and Development, 2017,54(7): 1439-1451.
[14]	张啸剑, 孟小峰 . 面向数据发布和分析的差分隐私保护[J]. 计算机学报, 2014,37(4): 927-949.
	ZHANG X J , MENG X F . Differential privacy protection for data Release and analysis[J]. Journal of Computer Science, 2014,37(4): 927-949.
[15]	熊平, 朱天清, 王晓峰 . 差分隐私保护及其应用[J]. 计算机学报, 2014,37(1): 101-122.
	XIONG P , ZHU T Q , WANG X F . Differential privacy protection and its application[J]. Journal of Computer Science, 2014,37(1): 101-122.
[16]	马鑫迪, 李辉, 马建峰 ,等. 轻量级位置感知推荐系统隐私保护框架[J]. 计算机学报, 2017,40(5): 1017-1030.
	MA X D , LI H , MA J F ,et al. Privacy protection framework of light-weight location-aware recommendation system[J]. Journal of Com-puter Science, 2017,40(5): 1017-1030.
[17]	姜火文, 曾国荪, 马海英 . 面向表数据发布隐私保护的贪心聚类匿名方法[J]. 软件学报, 2017,28(2): 341-351.
	JIANG H W , ZENG G S , MA H Y . Greedy clustering anonymous method for privacy protection of tabular data release[J]. Journal of Software, 2017,28(2): 341-351.
[18]	YANG M , ZHU T , MA L ,et al. Privacy preserving collaborative filtering via the Johnson-Lindenstrauss transform[C]// Trustcom/BigDataSE/ICESS. IEEE, 2017: 417-424.
[19]	刘海, 李兴华, 雒彬 ,等. 基于区块链的分布式 K 匿名位置隐私保护方案[J]. 计算机学报, 2019,42(5): 942-960.
	LIU H , LI X H , LUO B ,et al. Distributed K anonymous location privacy protection scheme based on block chain[J]. Journal of Com-puter Science, 2019,42(5): 942-960.
[20]	林荣智, 苗耀锋 . 基于用户项目特征分组的隐私保护算法[J]. 沈阳工业大学学报, 2018,40(6): 670-675.
	LIN R Z , MIAO Y F . Privacy protection algorithm based on user project feature grouping[J]. Journal of Shenyang University of Tech-nology, 2008,40(6): 670-675.
[21]	WANG P , YANG J , ZHANG J . A strategy toward collaborative filter recommended location service for privacy protection[J]. Sensors, 2018,18(5): 1522-1541.
[22]	LUO Z , CHEN Z . A privacy preserving group recommender based on cooperative perturbation[C]// International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. IEEE Computer Society, 2014: 106-111.
[23]	SHANG S , HUI Y , HUI P ,et al. Privacy preserving recommendation system based on groups[J]. Computer Science, 2013,5(1): 219-246.
[24]	WU Z , LI G , LIU Q ,et al. Covering the sensitive subjects to protect personal privacy in personalized recommendation[J]. IEEE Transactions on Services Computing, 2016,11(3): 493-506.
[25]	张玉洁, 杜雨露, 孟祥武 . 组推荐系统及其应用研究[J]. 计算机学报, 2016,39(4): 746-760.
	ZHANG Y J , DU Y L , MENG X W . Group recommendation system and its application research[J]. Journal of Computer Science, 2016,39(4): 746-760.
[26]	HE Y , ZHANG K , WANG H ,et al. Impact factor-based group recommendation scheme with privacy preservation in MSNs[C]// 2017 IEEE International Conference on Communications. IEEE, 2017.
[27]	YIN C , SHI L , SUN R ,et al. Improved collaborative filtering recommendation algorithm based on differential privacy protection[J]. The Journal of Super Computing, 2019(7): 1-14.
[28]	耿魁, 万盛, 李凤华 ,等. 基于隐私匹配的服务代理发现方法[J]. 通信学报, 2016,37(8): 136-143.
	GENG K , WAN S , LI F H ,et al. Service agent discovery method based on privacy matching[J]. Journal on Communications, 2016,37(8): 136-143.
[29]	张少波, 刘琴, 王国军 . 基于位置混淆的轨迹隐私保护方法[J]. 通信学报, 2018,39(7): 85-95.
	ZHANG S B , LIU Q , WANG G J . Trajectory privacy protection method based on location confusion[J]. Journal on Communications, 2018,39(7): 85-95.
[30]	王涛春, 刘盈, 金鑫 ,等. 群智感知中基于 k-匿名的位置及数据隐私保护方法研究[J]. 通信学报, 2018,39(S1): 176-184.
	WANG T C , LIU Y , JIN X ,et al. Research on k-anonymise-based location and data privacy protection methods in swarm intelligence perception[J]. Journal on Communications, 2018,39(S1): 176-184.

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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

Personalized privacy protection method for group recommendation

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