Computer Science ›› 2021, Vol. 48 ›› Issue (8): 300-308.doi: 10.11896/jsjkx.200900198

• Information Security • Previous Articles     Next Articles

Differentially Private Location Privacy-preserving Scheme withSemantic Location

ZHANG Xue-jun, YANG Hao-ying, LI Zhen, HE Fu-cun, GAI Ji-yang, BAO Jun-da   

  1. School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China
  • Received:2020-09-28 Revised:2020-12-15 Published:2021-08-10
  • About author:ZHANG Xue-jun,born in 1977,Ph.D,professor,is a senior member of China Computer Federation and a member of Association for Computing Machinery.His main research interests include data privacy and machine learning.
  • Supported by:
    National Natural Science Foundation of China(61762058) and Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University.

Abstract: How to realize more reasonable noise addition in location differential privacy-preserving is a hot topic issue.However,adding the same amount of noise in different locations will result in the decrease of service availability and privacy preservation.To this end,a differentially private location privacy-preserving scheme with semantic location is examined in this paper,which can systematically solve the contradiction among privacy-preserving,service availability and time overhead.The proposed method firstly constructs the expected distance by employing the framework of geo-indistinguishability,then determines the sensitivity of different locations by using the privacy quality function and requirement function,and finally adds Laplace noise to different types of region at fine granularity according to the location sensitivity.Comprehensive simulation experiments are carried out on two public datasets,which compare the proposed scheme with the existing methods in terms of query success rate based on Bayesian attack,service availability based on expected distance quantization and time overhead.The experimental results demonstrate that the proposed scheme is feasible and effective,and obtains a better trade-offs among privacy preservation,service availability and time consuming.

Key words: Differential privacy, Geo-indistinguishability, Location privacy, Location-based services, Semantic location

CLC Number: 

  • TP309
[1]JUNGLAS I A,WATSON R T.Location based services[J].Communications of the ACM,2008,51(3):65-69.
[2]ZHANG X J,GUI X L,WU Z D.Privacy preservation for location-based services:a survey[J].Journal of Software,2015,26(9):223-245.
[3]YAN G H,LIU T,ZHANG X J,et al.Service similarity location k anonymity privacy protection scheme against background knowledge inference attacks[J].Journal of Xi'an Jiaotong University,2020,54(1):8-18.
[4]ZHANG X J,HUANG H Y,HUANG S,et al.A Context-aware location differential perturbation scheme for privacy-aware users in mobile environment[J/OL].Wireless Communications & Mobile Computing,2018:1-15.
[5]SHOKRI R,THEODORAKOPOULOS G,TRONCOSO C,et al.Protecting location privacy:optimal strategy against localization attacks[C]// Proceedings of the 19th ACM SIGSAC Conference on Computer and Communications Security.ACM,2012:617-627.
[6]ANDRÉS M E,BORDENABE N E,CHATZIKOKOLAKIS K,et al.Geo-indistinguishability:Differential privacy for location-based system [C]//Proceedings of the 20th ACM SIGSAC Conference on Computer and Communications Security.ACM,2013:901-914.
[7]PRIMAULT V,MOKHTAR S B,LAURADOUX C,et al.Differentially private location privacy in practice[C]//Proceedings of the Third Workshop on Mobile Security Technologies.IEEE,2014:hal-01148230.
[8]BORDENABE N E,CHARZIKOKOLAKIS K,PALAMIDESSI C.Optimal geo-Indistinguishable mechanisms for location privacy[C]//Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security.ACM,2014:251-262.
[9]XIAO Y H,XIONG L.Protecting locations with differential privacy under temporal correlations[C]//Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security.ACM,2017:1298-1309.
[10]OYA S,TRONCOSO C.Is Geo-indistinguishability what youare looking for?[C]//Proceedings of the 2017 on Workshop on Privacy in the Electronic Society.ACM,2017:137-140.
[11]DING Z Y,WANG Y X,WANG G H,et al.Detecting violations of differential privacy[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.ACM,2018:475-489.
[12]WANG L Y.Geographic local differential privacy in crowdsen-sing:current states and future opportunities[J].Computer Science,2021,48(6):301-305.
[13]WANG M N,PENG C G,HE W Z,et al.Privacy metric model of differential privacy via graph theory and mutual information [J].Computer Science,2020,47(4):270-277.
[14]LEONHARDT U.Supporting location-awareness in open dis-tributed system[D].London:Imperial College of Science,Technology and Medicine University of London,1998.
[15]PETER I,MATTHIAS H.Highly available location-based ser-vices in mobile environments[C]//International Service Availability Symposium:Service Availability.Springer,2004,LNCS (3305):134-147.
[16]ZHAO D P,LUE Z P,ZHANG X G.Location and its semantics in location-based services [J].Geo Spatial Information Science,2007,10(2):145-150.
[17]LEE B,OH J,YU H,et al.Protecting location privacy using location semantics[C]//Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2011:1289-1297.
[18]CHATZIKOKOLAKIS K,PALAMIDESSI C,STRONATI M.Constructing elastic distinguishability metrics for location privacy[C]//Proceedings on Privacy Enhancing Technologies.Springer,2015(2):156-170.
[19]BINDSCHAEDLER V,SHOKRI R.Synthesizing plausible privacy-preserving location traces[C]//Proceedings of the 2016 IEEE Symposium on Security and Privacy.IEEE,2016:546-563.
[20]WANG Y L,ZUO K Z,ZENG H Y,et al.Sensitive-Semantic Location Privacy Protection for Continuous Query [J].Compu-ter Engineering and Applications,2020,56(14):74-81.
[21]QIU G Y,GUO D K,SHEN Y L,et al.Mobile semantic-aware trajectory for personalized location privacy preservation [J].IEEE Internet of Things Journal,2020(99):1.
[22]OpenStreetMap (OSM)[EB/OL].[2020-07-01].https://www.ope
[23]SHOKRI R.Privacy games:optimal user-centric data obfuscation[J].Proceedings on Privacy Enhancing Technologies,2015,2015(2):299-315.
[1] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[2] WANG Lei, LI Xiao-yu. LBS Mobile Privacy Protection Scheme Based on Random Onion Routing [J]. Computer Science, 2022, 49(9): 347-354.
[3] HUANG Jue, ZHOU Chun-lai. Frequency Feature Extraction Based on Localized Differential Privacy [J]. Computer Science, 2022, 49(7): 350-356.
[4] WANG Mei-shan, YAO Lan, GAO Fu-xiang, XU Jun-can. Study on Differential Privacy Protection for Medical Set-Valued Data [J]. Computer Science, 2022, 49(4): 362-368.
[5] KONG Yu-ting, TAN Fu-xiang, ZHAO Xin, ZHANG Zheng-hang, BAI Lu, QIAN Yu-rong. Review of K-means Algorithm Optimization Based on Differential Privacy [J]. Computer Science, 2022, 49(2): 162-173.
[6] DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35.
[7] SUN Lin, PING Guo-lou, YE Xiao-jun. Correlation Analysis for Key-Value Data with Local Differential Privacy [J]. Computer Science, 2021, 48(8): 278-283.
[8] CHEN Tian-rong, LING Jie. Differential Privacy Protection Machine Learning Method Based on Features Mapping [J]. Computer Science, 2021, 48(7): 33-39.
[9] WANG Hui, ZHU Guo-yu, SHEN Zi-hao, LIU Kun, LIU Pei-qian. Dummy Location Generation Method Based on User Preference and Location Distribution [J]. Computer Science, 2021, 48(7): 164-171.
[10] WANG Le-ye. Geographic Local Differential Privacy in Crowdsensing:Current States and Future Opportunities [J]. Computer Science, 2021, 48(6): 301-305.
[11] PENG Chun-chun, CHEN Yan-li, XUN Yan-mei. k-modes Clustering Guaranteeing Local Differential Privacy [J]. Computer Science, 2021, 48(2): 105-113.
[12] WANG Mao-ni, PENG Chang-gen, HE Wen-zhu, DING Xing, DING Hong-fa. Privacy Metric Model of Differential Privacy via Graph Theory and Mutual Information [J]. Computer Science, 2020, 47(4): 270-277.
[13] WU Ying-jie, HUANG Xin, GE Chen, SUN Lan. Adaptive Parameter Optimization for Real-time Differential Privacy Streaming Data Publication [J]. Computer Science, 2019, 46(9): 99-105.
[14] LI Lan, YANG Chen, WANG An-fu. Study on Selection of Privacy Parameters ε in Differential Privacy Model [J]. Computer Science, 2019, 46(8): 201-205.
[15] ZHOU Yi-hua, LI Guang-hui, YANG Yu-guang, SHI Wei-min. Location Privacy Preserving Nearest Neighbor Querying Based on GeoHash [J]. Computer Science, 2019, 46(8): 212-216.
Full text



No Suggested Reading articles found!