基于推荐的社交媒体数据发布的隐私保护
Privacy Protection Based on the Publication of Recommended Social Media Data
摘要: 个性化的推荐需要使用大量的用户数据，尤其是用户在社交媒体上的活动数据，包括评级、签到等，然而，从大量的用户活动数据中，能够推断出用户的隐私数据。在本文中，针对FM推荐算法的特性，提出距离度量KFC，约束数据失真，提出了PrivFM，一个可定制的、连续的、保护隐私的社交媒体数据发布框架，通过扰乱用户发布的活动数据，防止推理攻击，同时保证推荐效用。实验结果表明，相对于其他的隐私保护方法及距离度量，提高了隐私保护与推荐之间的平衡。
Abstract: Personalized recommendation requires the use of a large amount of user data, especially the activity data of users on social media, including ratings, checkins, etc. However, from a large amount of user activity data, users’ privacy data can be inferred. In this paper, aiming at the characteristics of FM recommendation algorithm, distance measurement KFC (Kendall feature correlation) was proposed to constrain data distortion, and PrivFM, a customizable, continuous and privacyprotecting social media data publishing framework, was proposed to prevent inference attacks by disrupting the active data published by users, while ensuring the recommendation effectiveness. The experimental results show that compared with other privacy protection methods and distance measurement, the balance between privacy protection and recommendation is improved.
文章引用：张兴兰, 杨捷. 基于推荐的社交媒体数据发布的隐私保护[J]. 计算机科学与应用, 2020, 10(3): 427-436. https://doi.org/10.12677/CSA.2020.103044

参考文献

[1] Salamatian, S., Zhang, A., Calmon, F.D.P., Bhamidipati, S., Fawaz, N., Kveton, B., Oliveira, P. and Taft, N. (2013) How to Hide the Elephant- or the Donkey- in the Room: Practical Privacy against Statistical Inference for Large Data. [Google Scholar] [CrossRef] 
[2] Yang, D., Zhang, D., Qu, B., et al. (2016) PrivCheck: Privacy-Preserving Check-in Data Publishing for Personalized Location Based Services. In: Proceedings of 2016 ACM International Joint Conference, ACM, New York. [Google Scholar] [CrossRef] 
[3] Li, C., Shirani-Mehr, H. and Yang, X.C. (2007) Protecting Individual Information against Inference Attacks in Data Publishing. Advances in Databases: Concepts, Systems and Applications, Proceedings of 12th International Conference on Database Systems for Advanced Applications, DASFAA 2007, Bangkok, Thailand, 9-12 April 2007. [Google Scholar] [CrossRef] 
[4] Chen, B.-C., Kifer, D., LeFevre, K. and Machanavajjhala, A. (2009) Privacy-Preserving Data Publishing. Foundations and Trends in Databases, 2, 1-167. [Google Scholar] [CrossRef] 
[5] 李杨, 温雯, 谢光强. 差分隐私保护研究综述[J]. 计算机应用研究, 2012, 29(9): 3201-3205.
[6] Sankar, L., Rajagopalan, S.R. and Poor, H.V. (2013) Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach. IEEE Transactions on Information Forensics and Security, 8, 838-852. [Google Scholar] [CrossRef] 
[7] Wagner, I. and Eckhoff, D. (2015) Technical Privacy Metrics: A Systematic Survey. arXiv preprint arXiv:1512.00327
[8] 彭长根, 丁红发, 朱义杰, 等. 隐私保护的信息熵模型及其度量方法[J]. 软件学报, 2016, 27(8): 1891-1903.
[9] Bhamidipati, S., Fawaz, N., Kveton, B. and Zhang, A. (2015) PriView: Personalized Media Consumption Meets Privacy against Inference Attacks. IEEE Software, 32, 53-59. [Google Scholar] [CrossRef] 
[10] Yang, D.Q., Qu, B.Q. and Philippe, C.M. (2019) Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation. IEEE Transactions on Knowledge and Data Engineering, 31, 507-520. [Google Scholar] [CrossRef] 
[11] 何凌云, 洪良怡, 周洁, 陈湃卓, 赵序琦, 谢宇明, 刘功申. 社交网络隐私安全研究综述[J]. 信息技术, 2018(5): 153-159.
[12] du Pin Calmon, F. and Fawaz, N. (2012) Privacy against Statistical Inference. [Google Scholar] [CrossRef] 
[13] Guignard, D. (2019) Partial Differential Equations with Random Input Data: A Perturbation Approach. Archives of Computational Methods in Engineering, 26, 1313-1377. [Google Scholar] [CrossRef] 
[14] Fan, W., He, J., Guo, M., Li, P., Han, Z. and Wang, R.C. (2020) Privacy Preserving Classification on Local Differential Privacy in Data Centers. Journal of Parallel and Distributed Computing, 135, 70-82.