差分隐私轨迹数据保护方案

doi:10.13190/j.jbupt.2021-076

北京邮电大学学报 ›› 2022, Vol. 45 ›› Issue (1): 13-18.doi: 10.13190/j.jbupt.2021-076

差分隐私轨迹数据保护方案

宋成, 许标, 贺军义

河南理工大学计算机科学与技术学院, 焦作 454000

收稿日期:2021-04-28 出版日期:2022-02-28 发布日期:2021-12-16
通讯作者: 贺军义(1982—),男,副教授,硕士生导师,邮箱:hjy@hpu.edu.cn E-mail:hjy@hpu.edu.cn
作者简介:宋成(1980—),男,副教授,硕士生导师
基金资助:
国家自然科学基金项目（61872126，61772159）；河南省科技攻关计划项目（192102210123，182102110333）

Differential Privacy Trajectory Data Protection Scheme

SONG Cheng, XU Biao, HE Junyi

School of Compute Science and Technology, Henan Polytechnic University, Jiaozuo 454000, China

Received:2021-04-28 Online:2022-02-28 Published:2021-12-16

摘要/Abstract

摘要： 针对当前用户轨迹隐私信息易泄露的问题，提出了一种差分隐私轨迹数据保护方案。该方案在基于位置服务请求用户身份匿名化处理的基础上，采用差分隐私技术对用户的轨迹数据集进行加噪生成轨迹噪声数据集；然后用轨迹加噪后的伪用户替代真实用户执行基于位置的服务请求，从而实现用户身份、轨迹和查询信息的隐私保护。通过安全性分析，所提方案具有匿名性、不可伪造性和抗假冒攻击等安全特性。仿真实验表明，所提方案不仅具有较好的数据可用性，也有一定的效率优越性。

关键词: 基于位置的服务, 差分隐私, 轨迹隐私, 椭圆曲线密码体制

Abstract: To solve the issue that the trajectory privacy information of current users is easy to be disclosed, a differential privacy trajectory data protection scheme is proposed. Based on the location-based service requesting user identity anonymization process, this scheme adopts differential privacy technology to add noise to the user's trajectory data set to generate the data set with trajectory noise. Then, the dummy user with noise-added trajectory is used to replace the real user to execute the location based service request, thereby realizing the privacy protection of the user's identity, trajectory and query information. Through security analysis, the scheme satisfies the security features of anonymity, unforgeability and anti-counterfeiting attack. The simulation results show that the scheme not only has better data availability, but also has certain advantages in efficiency.

Key words: location based services, differential privacy, trajectory privacy, elliptic curve cryptosystem

中图分类号:

TP309

宋成, 许标, 贺军义. 差分隐私轨迹数据保护方案[J]. 北京邮电大学学报, 2022, 45(1): 13-18.

SONG Cheng, XU Biao, HE Junyi. Differential Privacy Trajectory Data Protection Scheme[J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(1): 13-18.

参考文献

[1] HE Y, CHEN J G. User location privacy protection mechanism for location-based services[J]. Digital Communications and Networks, 2021, 7(2):264-276.
[2] ZHANG S B, MAO X J, CHOO K R, et al. A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services[J]. Information Sciences, 2020, 527:406-419.
[3] PENG T, LIU Q, MENG D C, et al. Collaborative trajectory privacy preserving scheme in location-based services[J]. Information Sciences, 2017, 387:165-179.
[4] LI F H, ZHANG C, NIU B, et al. Efficient trajectory privacy protection scheme[J]. Journal on Communications, 2015, 36(12):114-123. 李凤华, 张翠, 牛犇, 等. 高效的轨迹隐私保护方案[J]. 通信学报, 2015, 36(12):114-123.
[5] ZHENG L, ZHANG J X. Location-aware privacy protection scheme in continuous location-based service[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(12):2437-2444. 郑磊, 张俊星. 面向连续位置服务的位置感知隐私保护方案[J]. 浙江大学学报(工学版), 2020, 54(12):2437-2444.
[6] ZHANG C. Generating dummies based on gravity model for user's trajectory privacy[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):57-66. 张翠. 基于重力模型生成假轨迹的隐私保护方法[J]. 北京邮电大学学报, 2017, 40(2):57-66.
[7] HWANG R H, HSUEH Y L, CHUNG H W. A novel time-obfuscated algorithm for trajectory privacy protection[J]. IEEE Transactions on Services Computing, 2014, 7(2):126-139.
[8] OUYANG J, YIN J, LIU S P, et al. An effective diffe-rential privacy transaction data publication strategy[J]. Computer Research and Development, 2014, 51(10):2195-2205. 欧阳佳, 印鉴, 刘少鹏, 等. 一种有效的差分隐私事务数据发布策略[J]. 计算机研究与发展, 2014, 51(10):2195-2205.
[9] CHEN R, ACS G, CASTELLUCCIA C. Differentially sequential data publication via variable-length n-grams[C]//Proc of ACM CCS'12. New York:ACM, 2012:638-649.
[10] KANG H Y, ZHANG S X, JIA Q Q. A method for time-series location data publication based on differential privacy[J]. Wuhan University Journal of Natural Sciences, 2019, 24(2):107-115.

差分隐私轨迹数据保护方案

Differential Privacy Trajectory Data Protection Scheme

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 8

编辑推荐

Metrics

本文评价

[1]	宋成, 程道晨, 倪水平. 个性化差分隐私的 k 匿名轨迹隐私保护方案[J]. 北京邮电大学学报, 2023, 46(3): 109-114.
[2]	杜茂康彭俊杰胡勇进肖玲. 满足差分隐私的逻辑回归矩阵分解推荐算法[J]. 北京邮电大学学报, 2023, 46(3): 115-120.
[3]	王永, 尹恩民, 冉珣. 基于BC聚类的差分隐私保护推荐算法[J]. 北京邮电大学学报, 2021, 44(2): 81-88.
[4]	宋成, 张亚东, 彭维平, 王磊, 刘志中. 基于相似轨迹替代查询的位置隐私保护方案研究[J]. 北京邮电大学学报, 2020, 43(1): 135-142.
[5]	宋成, 张亚东, 王磊, 刘志中, 晁浩. 基于DTW交换查询的轨迹隐私保护方案[J]. 北京邮电大学学报, 2018, 41(6): 97-102.
[6]	周长利, 蔡绍滨, 王田, 马春光. 路网环境下基于不经意传输的LBS隐私保护方法[J]. 北京邮电大学学报, 2017, 40(6): 37-42.
[7]	杜惠英, 杜武恭. 高效的周期性移动定位优化策略[J]. 北京邮电大学学报, 2017, 40(6): 80-85.
[8]	张翠. 基于重力模型生成假轨迹的隐私保护方法[J]. 北京邮电大学学报, 2017, 40(2): 57-66.