CLM：面向轨迹发布的差分隐私保护方法

doi:10.11959/j.issn.1000-436x.2017119

通信学报 ›› 2017, Vol. 38 ›› Issue (6): 85-96.doi: 10.11959/j.issn.1000-436x.2017119

CLM：面向轨迹发布的差分隐私保护方法

王豪^1,²,徐正全^1,²,熊礼治³,王涛^1,²

¹ 武汉大学测绘遥感信息工程国家重点实验室，湖北武汉 430079
² 武汉大学地球空间信息技术协同创新中心，湖北武汉 430079
³ 南京信息工程大学计算机与软件学院，江苏南京 210044

修回日期:2017-04-12 出版日期:2017-06-25 发布日期:2017-06-30
作者简介:王豪（1990-），男，河南驻马店人，武汉大学博士生，主要研究方向为数据挖掘、隐私保护。|徐正全（1962-），男，湖北黄冈人，博士，武汉大学教授，主要研究方向为数据挖掘、隐私保护、图像处理等。|熊礼治（1988-），男，湖北荆州人，博士，南京信息工程大学讲师，主要研究方向为图像处理、信息安全。|王涛（1985-），男，河南开封人，博士，武汉大学副研究员，主要研究方向为并行与分布式系统、轨迹挖掘。
基金资助:
国家自然科学基金资助项目(41671443);武汉市应用基础研究计划基金资助项目(2016010101010024);中美计算机科学研究中心开放基金资助项目(KJR16228);南京信息工程大学人才引进基金资助项目(2016r055)

CLM:differential privacy protection method for trajectory publishing

Hao WANG^1,²,Zheng-quan XU^1,²,Li-zhi XIONG³,Tao WANG^1,²

¹ State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China
² Collaborative Innovation Center for Geospatial Technology,Wuhan University,Wuhan 430079,China
³ School of Computer and Software,Nanjing University of Information Science and Technology,Nanjing 210044,China

Revised:2017-04-12 Online:2017-06-25 Published:2017-06-30
Supported by:
Applied Basic Research Program of Wuhan(41671443);The National Natural Science Foundation of China(2016010101010024);Open Funding of NUIST and PAPD(KJR16228);Introducing Talenet of NUIST Program(2016r055)

摘要/Abstract

摘要：

针对现有轨迹差分隐私保护发布方法面临的独立噪声容易被滤除的问题，提出一种轨迹差分隐私发布方法——CLM。CLM 提出一种相关性拉普拉斯机制，利用高斯噪声通过特定的滤波器，产生与原始轨迹序列自相关函数一致的相关性噪声序列，叠加到原始轨迹中并发布。实验结果表明，与现有的轨迹差分隐私保护发布方法相比，CLM能够达到更高的隐私保护强度并能保证较好的数据可用性。

关键词: 轨迹发布, 隐私保护, 差分隐私, 相关性拉普拉斯

Abstract:

In order to solve the problem existing in differential privacy preserving publishing methods that the independent noise was easy to be filtered out,a differential privacy publishing method for trajectory data (CLM),was proposed.A correlated Laplace mechanism was presented by CLM,which let Gauss noises pass through a specific filter to produce noise whose auto-correlation function was similar with original trajectory series.Then the correlated noise was added to the original track and the perturbed track was released.The experimental results show that the proposed method can achieve higher privacy protection and guarantee better data utility compared with existing differential privacy preserving publishing methods for trajectory data.

Key words: trajectory publishing, privacy preserving, differential privacy, correlated Laplace

中图分类号:

TP309.2

王豪,徐正全,熊礼治,王涛. CLM：面向轨迹发布的差分隐私保护方法[J]. 通信学报, 2017, 38(6): 85-96.

Hao WANG,Zheng-quan XU,Li-zhi XIONG,Tao WANG. CLM:differential privacy protection method for trajectory publishing[J]. Journal on Communications, 2017, 38(6): 85-96.

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参考文献 33

[1]	ZHENG Y . Trajectory data mining:an overview[J]. ACM Transactions on Intelligent Systems ＆Technology, 2015,6(3): 1-41.
[2]	张扶桑, 金蓓弘, 汪兆洋 ,等. 基于轨迹挖掘的公交车自组织网络路由机制[J]. 计算机学报, 2015,38(3): 648-662.
	ZHANG F S , JIN B H , WANG Z Y ,et al. Routing mechanism of ad hoc network for bus based on trajectory mining[J]. Chinese Journal of Computers, 2015,38(3): 648-662.
[3]	宋雪涛, 蒲英霞, 刘大伟 ,等. 利用行人轨迹挖掘城市区域功能属性[J]. 测绘学报, 2015,44(s1): 82-88.
	SONG X T , PU Y X , LIU D W ,et al. Mining urban regional functional attributes by pedestrian trajectory[J]. Acta Geodaetica et Cartographica Sinica, 2015,44(s1): 82-88.
[4]	HUA J , GAO Y , ZHONG S . Differentially private publication of general time-serial trajectory data[C]// Computer Communications. 2015: 549-557.
[5]	CAO Y . Differentially private real-time data release over infinite trajectory streams[J]. IEICE Transactions on Information ＆Systems, 2016,E99-D(No.1): 68-73.
[6]	CHEN R , FUNG B C M , MOHAMMED N ,et al. Privacy-preserving trajectory data publishing by local suppression[J]. Information Sciences, 2013,231(1): 83-97.
[7]	CHOW C Y , MOKBEL M F . Privacy of spatial trajectories[M]// Computing with Spatial Trajectories, 2011: 109-141.
[8]	GEDIK B , LIU L . Protecting location privacy with personalized k-anonymity:architecture and algorithms[J]. IEEE Transactions on Mobile Computing, 2008,7(1): 1-18.
[9]	王璐, 孟小峰 . 位置大数据隐私保护研究综述[J]. 软件学报, 2014,25(4): 693-712.
	WANG L , MENG X F . A survey of the research on privacy preserving for location data[J]. Journal of Software, 2014,25(4): 693-712.
[10]	MAGLOGIANNIS I , KAZATZOPOULOS L , DELAKOURIDIS K ,et al. Enabling location privacy and medical data encryption in patient telemonitoring systems[J]. IEEE Transactions on Information Technology in Biomedicine a Publication of the IEEE Engineering in Medicine ＆Biology Society, 2009,13(6): 946-954.
[11]	DWORK C . Differential privacy[M]// Automata,Languages and Programming. Springer Berlin Heidelberg, 2006: 1-12.
[12]	SHEN E , YU T . Mining frequent graph patterns with differential privacy[C]// The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013: 545-553.
[13]	YANG B , SATO I , NAKAGWA H . Bayesian differential privacy on correlated data[C]// SIGMOD/PODS. 2015: 747-762.
[14]	XIAO X . Differentially private data release:improving utility with wavelets and Bayesian networks[M]// Springer International Publishing, 2014: 25-35.
[15]	SHORKRI R . Privacy games:optimal protection mechanism design for Bayesian and differential privacy[J]. IEEE Transactions on Dependable ＆Secure Computing, 2014,11(3): 266-279.
[16]	ZHU T , XIONG P , LI G ,et al. Correlated differential privacy:hiding information in non-IID data set[J]. IEEE Transactions on Information Forensics ＆Security, 2015,10(2): 229-242.
[17]	RASTOGI V , NATH S . Differentially private aggregation of distributed time series with transformation and encryption[C]// The ACM SIGMOD International Conference on Management of Data. 2010: 735-746.
[18]	XIAO X , . Differentially private data release:improving utility with wavelets and Bayesian networks[C]// Springer International Publishing. 2014: 25-35.
[19]	XIAO X ,et al. Differential privacy via wavelet transforms[J]. IEEE Transactions on Knowledge ＆Data Engineering, 2009,23(8): 23-30.
[20]	JIANG W , XIE C , ZHANG Z . Wishart mechanism for differentially private principal components analysis[J]. Computer Science, 2015,9285(2): 458-473.
[21]	GEHRKE J , HAY M , LUI E ,et al. Crowd-blending privacy[C]// Cryptology Conference on Advances in Cryptology.Springer-Verlag New York. 2012: 479-496.
[22]	GEHRKE J , LUI E , PASS R . Towards privacy for social networks:a zero-knowledge based definition of privacy[C]// Theory of Cryptography - 8th Theory of Cryptography Conference. 2011: 432-449.
[23]	KIFER D , MACHANAVAJJHALA A . Pufferfish:a framework for mathematical privacy definitions[J]. ACM Transactions on Database Systems, 2014,39(1): 1-36.
[24]	HE X , MACHANAVAJJHALA A , DING B . Blowfish privacy:tuning privacy-utility trade-offs using policies[C]// The 2014 ACM SIGMOD International Conference on Management of Data. 2014: 1447-1458.
[25]	CAO L , OU Y , YU P S . Coupled behavior analysis with applications[J]. IEEE Transactions on Knowledge ＆Data Engineering, 2012,24(8): 1378-1392.
[26]	ZHOU Z H , SUN Y Y , LI Y F . Multi-instance learning by treating instances as non-IID samples[C]// The 26th Annual International Conference on Machine Learning. 2009: 1249-1256.
[27]	RASTOGI V , HAY M , MILLAU G ,et al. Relationship privacy:output perturbation for queries with joins[C]// The 28th ACM Sigmod-Sigact-sigart Symposium on Principles of Database Systems. 2009: 107-116.
[28]	DWORK C , ROTH A . The algorithmic foundations of differential privacy[J]. Foundations and Trends in Theoretical Computer Science, 2014,9(3-4): 211-407.
[29]	BOJAN , BASRAK . The sample autocorrelation function of non-linear time series[M]// University of Groningen, 2000.
[30]	WANG H , XU Z Q . CTS-DP:publishing correlated time-series data via differential privacy[J]. Knowledge-Based Systems, 2017,122: 167-179.
[31]	ZHENG Y , XIE X , MA W Y . GeoLife:a collaborative social networking service among user,location and trajectory[J]. Bulletin of the Technical Committee on Data Engineering, 2010,33(2): 32-39.
[32]	YUAN J , ZHENG Y , ZHANG C ,et al. T-drive:driving directions based on taxi trajectories[C]// ACM SIGSPATIAL International Symposium on Advances in Geographic Information Systems. 2010: 99-108.
[33]	WANG H , TERROVITIS M , MAMOULIS N . Location recommendation in location-based social networks using user check-in data[C]// ACM Sigspatial International Conference on Advances in Geographic Information Systems. 2013: 374-383.

CLM：面向轨迹发布的差分隐私保护方法

CLM:differential privacy protection method for trajectory publishing

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