Personalized privacy protection method for group recommendation

被引：0
|
作者：
Wang H. [1 ,2 ]
Lu J. [1 ]
机构：
[1] School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing
[2] Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing
来源：
Tongxin Xuebao/Journal on Communications | 2019年 / 40卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Group recommendation; K-anonymous; Personalized privacy protection; Randomized perturbation;
D O I：
10.11959/j.issn.1000-436x.2019183
中图分类号：
学科分类号：
摘要：
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. © 2019, Editorial Board of Journal on Communications. All right reserved.
引用
收藏
页码：106 / 115
页数：9
相关论文
共 30 条
  • [1] Sweeney L., k-anonymity: a model for protecting privacy, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10, 5, pp. 557-570, (2002)
  • [2] Kaplan E., Gursoy M.E., Nergiz M.E., Et al., Known sample attacks on relation preserving data transformations, IEEE Transactions on Dependable and Secure Computing, 1, 32, pp. 101-108, (2017)
  • [3] Ahmed K.W., Mouri I.J., Zaman R., Et al., A privacy preserving personalized group recommendation framework, International Conference on Advanced Computing, pp. 594-598, (2016)
  • [4] Lu Z., Shen H., A security-assured accuracy-maximised privacy preserving collaborative filtering recommendation algorithm, International Database Engineering and Applications Symposium, pp. 72-80, (2015)
  • [5] Li C., Palanisamy B., Joshi J., SocialMix: supporting privacy-aware trusted social networking services, IEEE International Conference on Web Services, pp. 115-122, (2016)
  • [6] Liu P., Li Y., Sang Y., Et al., Anonymity-based privacy preserving network data publication, Trustcom/BigDataSE/ISPA, pp. 823-828, (2017)
  • [7] Tang Q., Wang J., Privacy-preserving friendship-based recommender systems, IEEE Transactions on Dependable and Secure Computing, 15, 5, pp. 784-796, (2018)
  • [8] Ahmadi M., Ghahfarokhi B.S., Preserving privacy in location based mobile coupon systems using anonymous authentication scheme, International Iranian Society of Cryptology Conference on Information Security and Cryptology, pp. 60-65, (2016)
  • [9] Guo L., Zhang C., Fang Y., A trust-based privacy-preserving friend recommendation scheme for online social networks, IEEE Transactions on Dependable & Secure Computing, 12, 4, pp. 413-427, (2015)
  • [10] Peng T., Liu Q., Wang G., Enhanced location privacy preserving scheme in location-based services, IEEE Systems Journal, 11, 1, pp. 219-230, (2014)
123