基于位置服务的分布式差分隐私推荐方法研究

doi:10.12263/DZXB.20180835

电子学报 ›› 2021, Vol. 49 ›› Issue (1): 99-110.DOI: 10.12263/DZXB.20180835

基于位置服务的分布式差分隐私推荐方法研究

郑孝遥^1,2, 罗永龙^1,2, 汪祥舜^1,2, 孙丽萍^1,2, 陈付龙^1,2, 胡桂银^1,2, 汪小寒^1,2

1. 安徽师范大学计算机与信息学院, 安徽芜湖 241002;
2. 网络与信息安全安徽省重点实验室, 安徽芜湖 241002

收稿日期:2018-09-27 修回日期:2020-01-07 出版日期:2021-01-25
- 通讯作者:
- 罗永龙
- 作者简介:
- 郑孝遥 男,1981年2月出生,安徽芜湖人.安徽师范大学计算机与信息学院副教授,CCF和IEEE会员,研究方向为信息安全、个性化推荐等.E-mail:zxiaoyao_2000@163.com;汪祥舜 男,1992年10月出生,安徽安庆人.现为安徽师范大学计算机与信息学院在读硕士研究生,研究方向为推荐系统和信息安全;孙丽萍 女,1980年6月生,安徽宣城人.2000年毕业于安徽师范大学计算机教育专业,安徽师范大学计算机与信息学院教授.主要从事空间数据处理和智能计算等领域的研究工作;陈付龙 男,1978年5月出生,安徽霍邱人.安徽师范大学计算机与信息学院教授,研究方向为嵌入式与普适计算,物联网安全等;胡桂银 男,1980年12月出生,安徽舒城人.现为安徽师范大学计算机与信息学院教师,研究方向为信息安全;汪小寒 女,1978年12月出生,安徽枞阳人,安徽师范大学计算机与信息学院副教授,主要研究方向为智能计算和信息安全.
- 基金资助:
- 国家自然科学基金 (No.61672039，No.61972439，No.61602009）; 安徽省自然科学基金 (No.1808085MF172，No.1908085MF190）; 高校优秀青年人才支持计划重点项目 (No.gxyqZD2019010）

Research on Location-Based Distributed Differential Privacy Recommendation Method

ZHENG Xiao-yao^1,2, LUO Yong-long^1,2, WANG Xiang-shun^1,2, SUN Li-ping^1,2, CHEN Fu-long^1,2, HU Gui-ying^1,2, WANG Xiao-han^1,2

1. School of Computer and Information, Anhui Normal University, Wuhu, Anhui 241002, China;
2. Anhui Provincial Key Laboratory of Network and Information Security, Wuhu, Anhui 241002, China

Received:2018-09-27 Revised:2020-01-07 Online:2021-01-25 Published:2021-01-25
- Corresponding author:
- LUO Yong-long
- Supported by:
- National Natural Science Foundation of China (No.61672039, No.61972439, No.61602009); Natural Science Foundation of Anhui Province (No.1808085MF172, No.1908085MF190); Key Program of Excellent Young Talents Project in Higher Education (No.gxyqZD2019010)

摘要/Abstract

摘要： 随着移动互联网技术的迅速发展，传统的推荐系统已不能很好地适应基于位置的推荐服务，同时也面临隐私泄露的问题.本文针对上述问题，首先提出一种分布式隐私保护推荐框架，并利用差分隐私保护理论，设计基于分布式框架的奇异值分解推荐算法，同时利用保序加密函数实现用户请求位置的保护.理论分析和在两个真实的数据集上的实验表明，本文提出的方法不仅具有较强隐私保护能力，同时相比传统的几种推荐算法，也具有较好的推荐性能.

关键词: 推荐系统, 分布式框架, 位置服务, 隐私保护, 保序加密

Abstract: With the rapid development of mobile Internet technology, the traditional recommender system has not been well adapted to the location-based recommendation service, and it also faces the risk of privacy leaks. In this paper, a distributed privacy preserving recommendation framework is proposed, and a singular value decomposition recommendation algorithm based on distributed framework is designed by using the differential privacy theory. Furthermore, we use order preserving encryption function to protect user request location. Theoretical analysis and experiments on two real datasets show that the proposed method not only has stronger privacy protection ability, but also has better recommendation performance than traditional recommendation algorithms.

Key words: recommender system, distributed framework, location-based service, privacy preserving, order preserving encryption

中图分类号:

TP309

郑孝遥, 罗永龙, 汪祥舜, 等. 基于位置服务的分布式差分隐私推荐方法研究[J]. 电子学报, 2021, 49(1): 99-110.

ZHENG Xiao-yao, LUO Yong-long, WANG Xiang-shun, et al. Research on Location-Based Distributed Differential Privacy Recommendation Method[J]. Acta Electronica Sinica, 2021, 49(1): 99-110.

参考文献

[1] Ramakrishnan N,Keller B J,Mirza B J,et al.Privacy risks in recommender systems[J].IEEE Internet Computing,2001,5(6):54-62.
[2] Yu Z,Wong R K,Chi C H.Efficient role mining for context-aware service recommendation using a high-performance cluster[J].IEEE Transactions on Services Computing,2017,10(6):914-926.
[3] Bost R,Popa R A,Tu S,et al.Machine learning classification over encrypted data[A] Network and Distributed System Security Symposium[C].San Diego,USA:ISOC,2015.4324-4325.
[4] Polatidis N,Georgiadis C K,Pimenidis E,et al.Privacy-preserving collaborative recommendations based on random perturbations[J].Expert Systems with Applications,2016,71(C):18-25.
[5] Erkin Z,Veugen T,Toft T,et al.Generating private recommendations efficiently using homomorphic encryption and data packing[J].IEEE Transactions on Information Forensics & Security,2012,7(3):1053-1066.
[6] Liu A,Wang W,Li Z,et al.A Privacy-preserving framework for trust-oriented point-of-interest recommendation[J].IEEE Access,2018,6:393-404.
[7] Wang S,Tang J,Wang Y,et al.Exploring hierarchical structures for recommender systems[J].IEEE Transactions on Knowledge and Data Engineering,2018,30(6):1022-35.
[8] 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 & Data Engineering,2005,17(6):734-749.
[9] Liu Z,Qu W,Li H,et al.A hybrid collaborative filtering recommendation mechanism for P2P networks[J].Future Generation Computer Systems,2010,26(8):1409-1417.
[10] Gilburd B,Schuster A,Ran W.k-TTP:a new privacy model for large-scale distributed environments[A].Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].New York,USA:ACM,2004.563-568.
[11] Neves A R D M,Álvaro Marcos G.Carvalho,Ralha C G.Agent-based architecture for context-aware and personalized event recommendation[J].Expert Systems with Applications,2014,41(2):563-573.
[12] Bilenko M,Richardson M.Predictive client-side profiles for personalized advertising[A] Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining[C].San Diego,USA:ACM,2011.413-421.
[13] Veugen T,De Haan R,Cramer R,et al.A framework for secure computations with two non-colluding servers and multiple clients,applied to recommendations[J].IEEE Transactions on Information Forensics & Security,2015,10(3):445-457.
[14] Rahman M,Ballesteros J,Carbunar B,et al.Toward preserving privacy and functionality in geosocial networks[A].Proceedings of the 19th ACM Annual International Conference on Mobile Computing & Networking[C].Miami,USA:ACM,2013.207-210.
[15] L Bisheng,U Hengartner.Privacy-preserving social recommendations in geosocial networks[A].Proceedings of the 2013 Eleventh Annual International Conference on Privacy,Security and Trust (PST)[C].Tarragona,Spain:IEEE,2013.69-76.
[16] 鲜征征,李启良,黄晓宇,陆寄远,李磊.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,LU Ji-yuan,LI Lei.Differential privacy protection for collaborative filtering algorithms with explicit and implicit trust[J].Acta Electronica Sinica,2018,46(12):3050-3059.(in Chinese)
[17] 范利云,左万利,王英,王鑫.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,WANG Xin.Research on recommender system model based on differential privacy and time series[J].Acta Electronica Sinica,2017,45(9):2057-2064.(in Chinese)
[18] D Riboni,C Bettini.A platform for privacy-preserving geo-social recommendation of points of interest[A].Proceedings of the 14th International Conference on Mobile Data Management (MDM)[C].Washington,USA:IEEE,2013.347-349.
[19] C.Dwork.Differential privacy[J].Lecture Notes in Computer Science,2006,26(2):1-12.
[20] Zheng X,Luo Y,Sun L,et al.A novel social network hybrid recommender system based on hypergraph topologic structure[J].World Wide Web Journal,2018,21(4):985-1013.
[21] Xu Z,Chen L,Dai Y,et al.A dynamic topic model and matrix factorization-based travel recommendation method exploiting ubiquitous data[J].IEEE Transactions on Multimedia,2017,19(8):1933-1945.
[22] Lian D,Ge Y,Zhang F,et al.Scalable content-aware collaborative filtering for location recommendation[J].IEEE Transactions on Knowledge & Data Engineering,2018,30(6):1122-1135.
[23] Wang L,Zhang W,He X,et al.Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation[A].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining[C].London,UK:ACM,2018.2447-2456.
[24] Huang Z,Shan G,Cheng J,et al.TRec:an efficient recommendation system for hunting passengers with deep neural networks[J].Neural Computing and Applications,2019,31(s1):209-222.
[25] Seo S,Huang J,Yang H,et al.Interpretable convolutional neural networks with dual local and global attention for review rating prediction[A].Proceedings of the Eleventh ACM Conference on Recommender Systems[C].Como,Italy:ACM,2017.297-305.
[26] Wang Q,Yin H,Hu Z,et al.Neural memory streaming recommender networks with adversarial training[A].Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining[C].London,UK:ACM,2018.2467-2475.
[27] Koren Y,Bell R,Volinsky C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[28] Furukawa J.Request-based comparable encryption[A].European Symposium on Research in Computer Security[C].Egham,UK:Springer,2013.129-146

基于位置服务的分布式差分隐私推荐方法研究

Research on Location-Based Distributed Differential Privacy Recommendation Method

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	郭庆, 田有亮, 万良. 基于代理重加密的区块链数据受控共享方案[J]. 电子学报, 2023, 51(2): 477-488.
[2]	曹阳, 高旻, 余俊良, 范琪琳, 荣文戈, 文俊浩. 基于双图混合随机游走的社会化推荐模型[J]. 电子学报, 2023, 51(2): 286-296.
[3]	张少波, 原刘杰, 毛新军, 朱更明. 基于本地差分隐私的K-modes聚类数据隐私保护方法[J]. 电子学报, 2022, 50(9): 2181-2188.
[4]	康海燕, 冀源蕊. 基于本地化差分隐私的时序位置发布方案研究[J]. 电子学报, 2022, 50(9): 2222-2232.
[5]	丁毅, 沈薇, 李海生, 钟琼慧, 田明宇, 李洁. 面向CNN的区块链可信隐私服务计算模型[J]. 电子学报, 2022, 50(6): 1399-1409.
[6]	张帅, 高旻, 文俊浩, 熊庆宇, 唐旭. 基于自监督学习的去流行度偏差推荐方法[J]. 电子学报, 2022, 50(10): 2361-2371.
[7]	袁水莲, 皮德常, 胥萌. 基于差分隐私的轨迹隐私保护方法[J]. 电子学报, 2021, 49(7): 1266-1273.
[8]	朱健, 胡凯, 张伯钧. 智能合约的形式化验证方法研究综述[J]. 电子学报, 2021, 49(4): 792-804.
[9]	郭景峰, 朱晓松, 李爽. 直播电视推荐系统的评分预测算法研究[J]. 电子学报, 2020, 48(9): 1735-1740.
[10]	黄名选, 蒋曹清. 基于项权值排序挖掘的跨语言查询扩展[J]. 电子学报, 2020, 48(3): 568-576.
[11]	陈传明, 林文诗, 俞庆英, 罗永龙. 一种基于单点收益的轨迹隐私保护方法[J]. 电子学报, 2020, 48(1): 143-152.
[12]	刘露, 胡封晔, 牛亮, 彭涛. 异质网络中基于节点影响力的相似度度量方法[J]. 电子学报, 2019, 47(9): 1929-1936.
[13]	宋海娜, 罗涛, 韩新宇, 李剑峰. 面向多敏感值的个性化随机响应机制设计与分析[J]. 电子学报, 2019, 47(6): 1236-1243.
[14]	杨高明, 朱海明, 方贤进, 苏树智. 局部差分隐私约束的关联属性不变后随机响应扰动[J]. 电子学报, 2019, 47(5): 1079-1085.
[15]	罗恩韬, 陈淑红, 王文博, 张少波, PINIAL Khan-butt. 移动社交网络多密钥混淆的交友隐私保护方案研究[J]. 电子学报, 2018, 46(9): 2123-2130.