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

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, 孙丽萍, 陈付龙, 胡桂银, 汪小寒. 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, SUN Li-ping, CHEN Fu-long, HU Gui-ying, WANG Xiao-han. Research on Location-Based Distributed Differential Privacy Recommendation Method[J]. Acta Electronica Sinica, 2021, 49(1): 99-110.

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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

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