融合显/隐式信任协同过滤算法的差分隐私保护

doi:10.3969/j.issn.0372-2112.2018.12.032

电子学报 ›› 2018, Vol. 46 ›› Issue (12): 3050-3059.DOI: 10.3969/j.issn.0372-2112.2018.12.032

融合显/隐式信任协同过滤算法的差分隐私保护

鲜征征^1,2, 李启良², 黄晓宇³, 陆寄远¹, 李磊²

1. 广东金融学院互联网金融与信息工程学院, 广东广州 510521;
2. 中山大学数据科学与计算机学院, 广东广州 510006;
3. 华南理工大学经济与贸易学院, 广东广州 510006

收稿日期:2017-06-13 修回日期:2018-04-22 出版日期:2018-12-25
- 通讯作者:
- 黄晓宇
- 作者简介:
- 鲜征征 女.1977年8月出生于四川省阆中市.博士,现为广东金融学院讲师,CCF会员.主要研究方向为数据挖掘、隐私保护等.E-mail:xianzhengzheng@126.com;李启良 男.1990年5月出生于广东省云浮市.硕士,现为华为技术有限公司集成服务部的软件工程师.主要研究方向为数据挖掘和隐私保护.E-mail:liqiliang90@163.com
- 基金资助:
- 广东省自然科学基金 (No.2017A030313391）; 广东省科技计划 (No.2017A050501042，No.2016ZC0039，No.2017ZC0117）; 广东省哲学社科 (No.GD15CGL05）

Differential Privacy Protection for Collaborative Filtering Algorithms with Explicit and Implicit Trust

XIAN Zheng-zheng^1,2, LI Qi-liang², HUANG Xiao-yu³, LU Ji-yuan¹, LI Lei²

1.School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, Guangdong 510521, China;
2.School of Data and Computer Science, Sun Yat-sen University, Guangzhou, Guangdong 510006, China;
3.School of Economics and Commerce, South China University of Technology, Guangzhou, Guangdong 510006, China

Received:2017-06-13 Revised:2018-04-22 Online:2018-12-25 Published:2018-12-25
- Corresponding author:
- HUANG Xiao-yu
- Supported by:
- National Natural Science Foundation of Guangdong Province, China (No.2017A030313391); Science and Technology Project of Guangdong Provicne (No.2017A050501042, No.2016ZC0039, No.2017ZC0117); Guangdong Philosophy and Social Science Foundation (No.GD15CGL05)

摘要/Abstract

摘要： 融合显/隐式信任关系的社会化协同过滤算法TrustSVD在推荐系统中有广泛的应用，但该算法存在用户隐私泄漏的风险.基于背景知识的用户个人隐私信息推断是当前Internet用户隐私信息泄漏的巨大隐患之一，差分隐私作为一种能为保护对象提供严格的理论保证的隐私保护机制而备受关注.本文把差分隐私保护技术引入TrustSVD中，提出了具有隐私保护能力的新模型DPTrustSVD.理论分析和实验结果显示，DPTrustSVD不仅为用户的隐私信息提供了严格的理论保证，而且仍然保持了较高的预测准确率.

关键词: 社会化协同过滤, 个人隐私保护, 差分隐私, 矩阵分解, 信任关系, 隐式信任

Abstract: TrustSVD, a popular social collaborative filtering algorithm that incorporates both of the explicit and implicit trust information, has been widely used in recommender systems. However, there is a risk of disclosure of user privacy in TrustSVD. Privacy information inference based on background knowledge is one of the great hidden dangers of user's privacy disclosure. Differential privacy has attracted much attentiaon as a privacy protection mechanism that can provide a strict theoretical guarantee for protection objects. In this article, we propose DPTrustSVD, a novel collaborative filtering algorithm that applies Differential privacy to TrustSVD and has the ability of privacy preserving. Theoretical analysis and experimental results show that DPTrustSVD not only provides a strict theoretical guarantee for users' privacy information, but also maintains a high prediction accuracy.

Key words: social collaborative filtering, personal privacy preservation, differential privacy, matrix factorization, trust relationship, implicit trust

中图分类号:

TP311

鲜征征, 李启良, 黄晓宇, 等. 融合显/隐式信任协同过滤算法的差分隐私保护[J]. 电子学报, 2018, 46(12): 3050-3059.

XIAN Zheng-zheng, LI Qi-liang, HUANG Xiao-yu, et al. Differential Privacy Protection for Collaborative Filtering Algorithms with Explicit and Implicit Trust[J]. Acta Electronica Sinica, 2018, 46(12): 3050-3059.

参考文献

[1] ADOMAVICIUS G,TUZHILIN A.Toward the next generation of recommender systems:A survey of the state-of-the-art and possible extensions[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(6):734-749.
[2] YANG Bo,LEI Yu,LIU Da-you et al.Social collaborative filtering by trust[A].Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI'13)[C].Beijing,China:IJCAI,2013.2747-2753.
[3] 俞春花,刘学军,李斌,等.基于上下文相似度和社会网络的移动服务推荐方法[J].电子学报,2017,45(6):1530-1536. YU Chun-hua,LIU Xue-jun,LI Bin,et al.Mobile service recommendation based on context similarity and social network[J].Acta Electronica Sinica,2017,45(6):1530-1536.(in Chinese)
[4] JAMALI M,ESTER M.A transitivity aware matrix factorization model for recommendation in social networks[A].Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI'11)[C].Barcelona,Catalonia,Spain:IJCAI,2011.2644-2649.
[5] YAO Wei-long,HE Jing,HUANG Guang-yan,et al.Modeling dual role preferences for trust-aware recommendation[A].Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR'14)[C].New York,US:ACM,2014.975-978.
[6] GUO Gui-bing,ZHANG Jie,NEIL Y S.TrustSVD:Collaborative filtering with both the explicit and implicit influence of user trust and of item ratings[A].Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI'15)[C].Austin,Texas:AAAI,2015.123-129.
[7] GUO Gui-bing,ZHANG Jie,NEIL Yorke-Smith.A novel recommendation model regularized with user trust and item ratings[J].IEEE Transactions on Knowledge and Data Engineering,2016,28(7):1607-1620.
[8] 曹珍富,董晓蕾,周俊,等.大数据安全与隐私保护研究进展[J].计算机研究与发展,2016,53(10):2137-2151. CAO Zhen-fu,DONG Xiao-lei,ZHOU Jun,et al.Research advances on big data security and privacy preserving[J].Journal of Computer Research and Development,2016,53(10):2137-2151.(in Chinese)
[9] DWORK C.Differential privacy[A].Proceedings of the 33rd International Colloquium on Automata,Languages and Programming,Venice (ICALP'06)[C].Italy:ICALP,2006.1-12.
[10] 张啸剑,孟小峰.面向数据发布和分析的差分隐私保护研究[J].计算机学报,2014,37(4):927-949. ZHANG Xiao-jian,MENG Xiao-feng.Differential privacy in data publication and analysis[J].Chinese Journal of Computers,2014,37(4):927-949.(in Chinese)
[11] MCSHERRY F,MIRONOV I.Differential private recommender system:Building privacy into Netflix prize conteders[A].Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'09)[C].New York:ACM,2009.627-636.
[12] HARDT M,ROTH A.Beating randomized response on incoherent matrices[A].Proceeding of the Annual Acm Symposium on Theory of Computing (STOC'12)[C].New York:ACM,2012.1255-1268.
[13] FRIEDMAN A,BERLIOZ A,KAAFAR S,et al.A differential privacy framework for matrix factorization recommender systems[J].User Modeling and User-Adapted Interaction,2016,26(5):1-34.
[14] HUA Jing-yu,XIA Chang,ZHONG Sheng.Differentially private matrix factorization[A].Proceeding of the 24th International Conference on Artificial Intelligence(IJCAI'15)[C].New York:ACM,2015.1763-1770.
[15] YAN Shen,PAN Shi-ran,ZHU Wen-tao,et al.DynaEgo:Privacy-preserving collaborative filtering recommender system based on social-aware differential privacy[A].International Conference on Information and Communications Security(ICICS'16)[C].US:ICICS,2016.347-357.
[16] 范利云,左万利,王英,等一种基于差分隐私和时序的推荐系统模型研究[J].电子学报,2017,45(9):2057-2064. FAN Li-yun,ZUO Wan-li,WANG ying,et al.Research on recommender system model based on differential privacy and time series[J].Acta Electronica Sinica,2017,45(9):2057-2064.(in Chinese)
[17] KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[18] DWORK C.Differential privacy:A survey of results[A].Proceeding of Theory and Applications of Models of Computation (TAMC'08)[C].US:TAMC,2008.1-9.
[19] MCSHERRY F.Privacy integrated queries:An extensible platform for privacy-preserving data analysis[A].Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD'09)[C].Providence,Rhode Island:ACM,2009.19-30.
[20] NISSIM K,RASKHODNIKOVA S,SMITH A.Smooth sensitivity and sampling in private data analysis[A].Proceedings of the 39th Annual ACM Symposium on Theroy of Computing (STOC'07)[C].US:ACM,2007.75-84.
[21] DWORK C,MCSHERRY F,NISSIM K,SMITH A.Calibrating noise to sensitivity in private data analysis[A].Proceedings of the 3th Theory of Cryptography Conference (TCC'06)[C].US,2006.363-385.
[22] 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 Ping,ZHU Tian-qing,WANG Xiao-feng.A survey on differential privacy and applications[J].Chinese Journal of Computer,2014,37(1):101-122.(in Chinese)
[23] CHAUDHURI K,MONTELEONI C,SARWATE A.Differentially private empirical risk minimization[J].Journal of Machine Learning Research,2011,12(2):1069-1109.
[24] SHALEV-SHWARTZ S,SREBRO N.SVM optimization:Inverse dependence on training set size[A].Proceedings of the 25th International Conference on Machine Learning (ICML'08)[C].US:ICML,2008.928-935.
[25] SU Kai,MA Liang-li,XIAO Bin,et al.Web service QoS prediction by neighbor information combined non-negative matrix factorization[J].Journal of Intelligent & Fuzzy Systems,2016,30(6):3593-3604.

融合显/隐式信任协同过滤算法的差分隐私保护

Differential Privacy Protection for Collaborative Filtering Algorithms with Explicit and Implicit Trust

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