基于预测和滑动窗口的轨迹差分隐私保护机制

doi:10.11959/j.issn.1000-436x.2020049

通信学报 ›› 2020, Vol. 41 ›› Issue (4): 123-133.doi: 10.11959/j.issn.1000-436x.2020049

基于预测和滑动窗口的轨迹差分隐私保护机制

叶阿勇^1,²,孟玲玉^1,²,赵子文^1,²,刁一晴^1,²,张娇美^1,²

¹ 福建师范大学数学与信息学院，福建福州 350007
² 福建省网络安全与密码技术重点实验室，福建福州 350007

修回日期:2020-01-30 出版日期:2020-04-25 发布日期:2020-04-30
作者简介:叶阿勇（1977– ），男，福建福州人，博士，福建师范大学教授，主要研究方向为无线网络技术、隐私与安全、信息服务等|孟玲玉（1994– ），女，黑龙江安达人，福建师范大学硕士生，主要研究方向为网络空间安全、位置隐私保护等|赵子文（1992– ），男，山东枣庄人，福建师范大学硕士生，主要研究方向为网络空间安全、位置隐私保护等|刁一晴（1997– ），女，山东济南人，福建师范大学硕士生，主要研究方向为网络空间安全、区块链隐私保护等|张娇美（1995– ），女，河南洛阳人，福建师范大学硕士生，主要研究方向为网络空间安全、机器学习隐私保护等
基金资助:
国家自然科学基金资助项目(61972096);国家自然科学基金资助项目(61872088);国家自然科学基金资助项目(61872090);福建省自然科学基金资助项目(2018J01780)

Trajectory differential privacy protection mechanism based on prediction and sliding window

Ayong YE^1,²,Lingyu MENG^1,²,Ziwen ZHAO^1,²,Yiqing DIAO^1,²,Jiaomei ZHANG^1,²

¹ College of Mathematics and Informatics,Fujian Normal University,Fuzhou 350007,China
² Fujian Provincial Key Laboratory of Network Security and Cryptology,Fuzhou 350007,China

Revised:2020-01-30 Online:2020-04-25 Published:2020-04-30
Supported by:
The National Natural Science Foundation of China(61972096);The National Natural Science Foundation of China(61872088);The National Natural Science Foundation of China(61872090);The Natural Science Foundation of Fujian Province(2018J01780)

摘要/Abstract

摘要：

为解决轨迹差分隐私保护中存在的隐私预算与服务质量等问题，提出了一种融合预测扰动的轨迹差分隐私保护机制。首先，利用马尔可夫链和指数扰动方法预测满足差分隐私和时空安全的扰动位置，并引入服务相似地图检测该位置的可用性；如果预测成功，则直接采用预测位置替代差分扰动的位置，以降低连续查询的隐私开销并提高服务质量。在此基础上，设计基于w滑动窗口的轨迹隐私预算分配机制，确保轨迹中任意连续的w次查询满足ε-差分隐私，解决连续查询的轨迹隐私问题。此外，基于敏感度地图设计一种隐私定制策略，通过自定义语义位置的隐私敏感度，实现隐私预算的量身定制，从而进一步提高其利用率。最后，利用真实数据集对所提方案进行实验分析，结果显示所提方案提供了更好的隐私保护水平和服务质量。

关键词: 位置隐私, 轨迹隐私, 差分隐私, 隐私累积

Abstract:

To address the issues of privacy budget and quality of service in trajectory differential privacy protection,a trajectory differential privacy mechanism integrating prediction disturbance was proposed.Firstly,Markov chain and exponential perturbation method were used to predict the location which satisfies the differential privacy and temporal and spatial security,and service similarity map was introduced to detect the availability of the location.If the prediction was successful,the prediction location was directly used to replace the location of differential disturbance,to reduce the privacy cost of continuous query and improve the quality of service.Based on this,the trajectory privacy budget allocation mechanism based on w sliding window was designed to ensure that any continuous w queries in the trajectory meet the ε-differential privacy and solve the trajectory privacy problem of continuous queries.In addition,a privacy customization strategy was designed based on the sensitivity map.By customizing the privacy sensitivity of semantic location,the privacy budget could be customized to improve its utilization.Finally,the validity of the scheme was verified by real data set experiment.The results illustrate that it offers the better privacy and quality of service.

Key words: location privacy, trajectory privacy, differential privacy, privacy accumulation

中图分类号:

TP309.2

叶阿勇,孟玲玉,赵子文,刁一晴,张娇美. 基于预测和滑动窗口的轨迹差分隐私保护机制[J]. 通信学报, 2020, 41(4): 123-133.

Ayong YE,Lingyu MENG,Ziwen ZHAO,Yiqing DIAO,Jiaomei ZHANG. Trajectory differential privacy protection mechanism based on prediction and sliding window[J]. Journal on Communications, 2020, 41(4): 123-133.

图/表 8

图1

图2

图3

图4

图5

图6

图7

图8

参考文献 17

[1]	MADDEN M , LENHART A , CORTESI S ,et al. Pew internet and american life project[J]. Washington,DC:Pew Research Center, 2010
[2]	DOBSON J E , FISHER P . Geoslavery[J]. Technology and Society Magazine, 2003,22(1): 47-52.
[3]	ANDRéS M E , BORDENABE N E , CHATZIKOKOLAKIS K . Geo-indistinguishability:differential privacy for location-based systems[C]// Proceedings of the 2013 ACM SIGSAC Conference on Computer ＆ Communications Security. New York:ACM Press, 2013: 901-914.
[4]	DWORK C , MCSHERRY F , NISSIM K . Calibrating noise to sensitivity in private data analysis[C]// Theory of Cryptography Conference. Berlin:Springer, 2006: 265-284.
[5]	GRUTESER M , GRUNWALD D . Anonymous usage of location-based services through spatial and temporal cloaking[C]// Proceedings of the 1st International Conference on Mobile Systems,Applications And Services. New York:ACM Press, 2003: 31-42.
[6]	NIU B , LI Q , ZHU X ,et al. Achieving k-anonymity in privacy-aware location-based services[C]// The 33rd Annual IEEE International Conference on Computer Communications. Piscataway:IEEE Press, 2014: 754-762.
[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, 2013,7(2): 126-139.
[8]	WANG J , LI Y , YANG D ,et al. Achieving effective k-anonymity for query privacy in location-based services[J]. IEEE Access, 2017(5): 24580-24592.
[9]	KIDO H , YANAGISAWA Y , SATOH T . Protection of location privacy using dummies for location-based services[C]// 21st International Conference on Data Engineering Workshops. Piscataway:IEEE Press, 2005: 1248-1248.
[10]	ANDRéS M E , BORDENABE N E , CHATZIKOKOLAKIS K . Geo-indistinguishability:differential privacy for location-based systems[C]// Proceedings of the 2013 ACM SIGSAC conference on Computer ＆ communications security. New York:ACM Press, 2013: 901-914.
[11]	XIAO Y , XIONG L . Protecting locations with differential privacy under temporal correlations[C]// ACM Sigsac Conference on Computer and Communications Security. New York:ACM Press, 2015: 1298-1309.
[12]	HUA J , TONG W , XU F . A geo-indistinguishable location perturbation mechanism for location-based services supporting frequent queries[J]. IEEE Transactions on Information Forensics and Security, 2017,13(5): 1155-1168.
[13]	吴云乘, 陈红, 赵素云 . 一种基于时空相关性的差分隐私轨迹保护机制[J]. 计算机学报, 2018,41(2): 309-322.
	WU Y C , CHEN H , ZHAO S Y . Differentially privacy trajectory protection based on spatial and temporal correlation[J]. Chinese Journal of Computers, 2018,41(2): 309-322.
[14]	CHATZIKOKOLAKIS K , PALAMIDESSI C , STRONATI M . A predictive differentially-private mechanism for mobility traces[M]// Privacy Enhancing Technologies. Berlin:Springer International Publishing, 2014.
[15]	叶阿勇, 李亚成, 马建峰 ,等. 基于服务相似性的 k-匿名位置隐私保护方法[J]. 通信学报, 2014,35(11): 162-169.
	YE A Y , LI Y C , MA J F ,et al. Location privacy-preserving method of k-anonymous based on service similarity[J]. Journal on Communications, 2014,35(11): 162-169.
[16]	ZHENG Y , XIE X , MA W Y . GeoLife:a collaborative social networking service among user,location and trajectory[J]. IEEE Data Engineering Bulletin, 2010,33(2): 32-39.
[17]	CHO E , MYERS S A , LESKOVEC J . Friendship and mobility:user movement in location-based social networks[C]// Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. New York:ACM Press, 2011: 1082-1090.

基于预测和滑动窗口的轨迹差分隐私保护机制

Trajectory differential privacy protection mechanism based on prediction and sliding window

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 17

相关文章 15

Metrics

推荐阅读 0

[1]	冯涛, 陈李秋, 方君丽, 石建明. 基于本地化差分隐私和属性基可搜索加密的区块链数据共享方案[J]. 通信学报, 2023, 44(5): 224-233.
[2]	余晟兴, 陈泽凯, 陈钟, 刘西蒙. DAGUARD：联邦学习下的分布式后门攻击防御方案[J]. 通信学报, 2023, 44(5): 110-122.
[3]	张淑芬, 董燕灵, 徐精诚, 王豪石. 基于目标扰动的AdaBoost算法[J]. 通信学报, 2023, 44(2): 198-209.
[4]	汤凌韬, 王迪, 刘盛云. 面向非独立同分布数据的联邦学习数据增强方案[J]. 通信学报, 2023, 44(1): 164-176.
[5]	袁程胜, 郭强, 付章杰. 基于差分隐私的深度伪造指纹检测模型版权保护算法[J]. 通信学报, 2022, 43(9): 181-193.
[6]	王瀚仪, 李效光, 毕文卿, 陈亚虹, 李凤华, 牛犇. 多级本地化差分隐私算法推荐框架[J]. 通信学报, 2022, 43(8): 52-64.
[7]	张勇, 李丹丹, 韩璐, 黄小红. 隐私保护的群体感知数据交易算法[J]. 通信学报, 2022, 43(5): 1-13.
[8]	康海燕, 冀源蕊. 基于本地化差分隐私的联邦学习方法研究[J]. 通信学报, 2022, 43(10): 94-105.
[9]	晏燕, 丛一鸣, Adnan Mahmood, 盛权政. 基于深度学习的位置大数据统计发布与隐私保护方法[J]. 通信学报, 2022, 43(1): 203-216.
[10]	陈思, 付安民, 苏铓, 孙怀江. 基于差分隐私的轨迹隐私保护方案[J]. 通信学报, 2021, 42(9): 54-64.
[11]	李洪涛, 任晓宇, 王洁, 马建峰. 基于差分隐私的连续位置隐私保护机制[J]. 通信学报, 2021, 42(8): 164-175.
[12]	蔡剑平, 刘西蒙, 熊金波, 应作斌, 吴英杰. 差分隐私下多重一致性约束问题的逼近方法[J]. 通信学报, 2021, 42(6): 107-117.
[13]	朱素霞, 刘抒伦, 孙广路. 基于相对熵和K-means的形状相似差分隐私轨迹保护机制[J]. 通信学报, 2021, 42(2): 113-123.
[14]	刘杨, 李珺, 陈文韵, 彭木根. 面向6G的雾无线接入网内生安全数据共享机制研究[J]. 通信学报, 2021, 42(1): 67-78.
[15]	王洁,王春茹,马建峰,李洪涛. 基于位置语义和查询概率的假位置选择算法[J]. 通信学报, 2020, 41(3): 53-61.