基于位置服务隐私自关联的隐私保护方案

doi:10.11959/j.issn.1000-436x.2019110

Abstract

Abstract:

The prevalence of mobile intelligent terminals gives the location-based service (LBS) more opportunities to enrich mobile users’ lives.However,mobile users enjoy the convenience with the cost of personal privacy.The side information and mobile user’s recent requirement records were considered,which were obtained or stored by the service provider.Based on the existence of recent requirement records,adversary can employ the inference attack to analysis mobile user’s personal information.Therefore,two schemes were proposed,including of basic privacy self-correlation privacy-preserving scheme (Ba-2PS) and enhanced privacy self-correlation privacy-preserving scheme(En-2PS).In En-2PS,the privacy-preserving scheme was designed from two dimensions of aspects of time factor and query region,which increased the uncertainty inferring out the real information.Finally,the privacy analysis was illustrated to proof En-2PS’s privacy degree,then the performance and privacy evaluation results indicate that En-2PS is effective and efficient.

Key words: location service, privacy preservation, location privacy, query privacy, k-anonymity

CLC Number:

TN929.5

Weihao LI,Jin CAO,Hui LI. Privacy self-correlation privacy-preserving scheme in LBS[J]. Journal on Communications, 2019, 40(5): 57-66.

Figures/Tables 9

References 28

[1]	LAI C , ZHOU H , CHENG N ,et al. Secure group communications in vehicular networks:a software-defined network-enabled architecture and solution[J]. IEEE Vehicular Technology Magazine, 2017,12(4): 40-49.
[2]	HE X , JIN R , DAI H . Leveraging spatial diversity for privacy-aware location-based services in mobile networks[J]. IEEE Transactions on Information Forensics and Security, 2018,13(6): 1524-1534.
[3]	ZHANG J , CHOW C Y . Enabling probabilistic differential privacy protection for location recommendations[J]. IEEE Transactions on Services Computing, 2018:1.
[4]	李凤华, 李晖, 贾焰 ,等. 隐私计算研究范畴及发展趋势[J]. 通信学报, 2016,37(4): 1-11.
	LI F H , LI H , JIA Y ,et al. Privacy computing:concept,connotation and its research trend[J]. Journal on Communications, 2016,37(4): 1-11.
[5]	NIU B , LI Q , ZHU X ,et al. Achieving k-anonymity in privacy-aware location-based services[C]// International Conference on Computer Communications. IEEE, 2014: 754-762.
[6]	LIU H , LI X , LI H ,et al. Spatiotemporal correlation-aware dummy-based privacy protection scheme for location-based services[C]// International Conference on Computer Communications. IEEE, 2017.
[7]	ZHANG P , HU C , CHEN D ,et al. Shiftroute:achieving location privacy for map services on smartphones[J]. IEEE Transactions on Vehicular Technology, 2018,67(5): 4527-4538.
[8]	ZHENG X , CAI Z , LI J ,et al. Location-privacy-aware review publication mechanism for local business service systems[C]// International Conference on Computer Communications. IEEE, 2017: 1-9.
[9]	JIANG H , ZHAO P , WANG C . Roblop:towards robust privacy preserving against location dependent attacks in continuous lbs queries[J]. IEEE/ACM Transactions on Networking, 2018,26(2): 1018-1032.
[10]	XIAO Y , XIONG L . Protecting locations with differential privacy under temporal correlations[C]// The 22nd ACM Conference on Computer and Communications Security. 2015: 1-7.
[11]	SHOKRI R , THEODORAKOPOULOS G , BOUDEC J Y L ,et al. Quantifying location privacy[C]// IEEE Security and Privacy. 2011: 247-262.
[12]	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.
[13]	LI W , NIU B , LI H ,et al. Privacy-preserving strategies in service quality aware location-based services[C]// IEEE International Conference on Communications. 2015: 7328-7334.
[14]	YI X , PAULET R , BERTINO E ,et al. Practical approximate K nearest neighbor queries with location and query privacy[J]. IEEE Transactions on Knowledge and Data Engineering, 2016,28(6): 1546-1559.
[15]	PAULET R , KAOSAR M , YI X ,et al. Privacy-preserving and content-protecting location based queries[J]. IEEE Transactions on Knowledge and Data Engineering, 2014,26(5): 1200-1210.
[16]	ANDRES M , BORDENABE N , CHATZIKOKOLAKIS K ,et al. Geo-indistinguishability:differential privacy for location-basedsystems[C]// The ACM Conference on Computer and Communications Security. 2013: 901-914.
[17]	PERAZZO P , DINI G . A uniformity-based approach to location privacy[J]. Computer Communications, 2015,64(1): 21-32.
[18]	XU T , CAI Y . Feeling-based location privacy protection for locationbased services[C]// ACM Conference on Computer and Communications Security. 2009: 348-357.
[19]	XU T , CAI Y . Exploring historical location data for anonymity preservation in location-based services[C]// International Conference on Computer Communications. IEEE, 2008: 1220-1228.
[20]	PALANISAMY B , LIU L . Attack-resilient mix-zones over road networks:architecture and algorithms[J]. IEEE Transactions on Mobile Computing, 2015,14(3): 495-508.
[21]	MEYEROWITZ J , CHOUDHURY R . Hiding stars with fireworks:location privacy through camouflage[C]// The Annual International Conference on Mobile Computing and Networking. 2009: 345-356.
[22]	MACHANAVAJJHALAA , GEHRKEJ , KIFERD ,et al. L-diversity:privacy beyond k-anonymity[C]// International Conference on Data Engineering. IEEE, 2006: 24-26.
[23]	TRIPATHYB K , MAITY A , RANAJIT B ,et al. A fast psensitive l-diversity anonymisation algorithm[C]// Recent Advances in Intelligent Computational Systems. IEEE, 2011: 741-744.
[24]	HE X , JIN R , DAH I . Leveraging spatial diversity for privacy-aware location-based services in mobile networks[J]. IEEE Transactions on Information Forensics and Security, 2018,13(6): 1524-1534.
[25]	HAO G , BINX Y . Research on privacy preserving method based on t-closeness model[C]// The International Conference on Communications in China. IEEE, 2017: 1455-1459.
[26]	MONEDERO D R , FORNE J , FERRER J D . From t-closeness-like privacy to post randomization via information theory[J]. IEEE Transactions on Knowledge and Data Engineering, 2010,22(11): 1623-1636.
[27]	LI N H , LIT C , VENKATASUBRAMANIAN S . T-closeness:privacy beyond k-anonymity and l-diversity[C]// International Conference on Data Engineering. IEEE, 2007: 106-115.
[28]	CHEN Z , HU X , JU X ,et al. Lisa:location information scrambler for privacy protection on smartphones[C]// Conference on Communications and Network Security. IEEE, 2013: 296-304.

Metrics

Recommended 0

No Suggested Reading articles found!

Privacy self-correlation privacy-preserving scheme in LBS

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 28

Related Articles 15

Metrics

Recommended 0

[1]	Yan YAN, Yiming CONG, Mahmood Adnan, Quanzheng SHENG. Statistics release and privacy protection method of location big data based on deep learning [J]. Journal on Communications, 2022, 43(1): 203-216.
[2]	Hongtao LI, Xiaoyu REN, Jie WANG, Jianfeng MA. Continuous location privacy protection mechanism based on differential privacy [J]. Journal on Communications, 2021, 42(8): 164-175.
[3]	Jie CUI, Xuefeng CHEN, Jing ZHANG, Lu WEI, Hong ZHONG. Bus cache-based location privacy protection scheme in the Internet of vehicles [J]. Journal on Communications, 2021, 42(7): 150-161.
[4]	Chen FANG, Yuanbo GUO, Yifeng WANG, Yongjin HU, Jiali MA, Han ZHANG, Yangyang HU. Edge computing privacy protection method based on blockchain and federated learning [J]. Journal on Communications, 2021, 42(11): 28-40.
[5]	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.
[6]	Jie WANG,Chunru WANG,Jianfeng MA,Hongtao LI. Dummy location selection algorithm based on location semantics and query probability [J]. Journal on Communications, 2020, 41(3): 53-61.
[7]	Zhu WANG,Kun HE,Xinyu WANG,Ben NIU,Fenghua LI. Traffic characteristic based privacy leakage assessment scheme for Android device [J]. Journal on Communications, 2020, 41(2): 155-164.
[8]	Wenjing ZHANG,Qiao LIU,Hui ZHU. Evaluation and protection of multi-level location privacy based on an information theoretic approach [J]. Journal on Communications, 2019, 40(12): 51-59.
[9]	Yuanzhi YAO,Feng WANG,Wenbo YAN,Nenghai YU. Image privacy preservation scheme based on QR code and reversible visible watermarking [J]. Journal on Communications, 2019, 40(11): 65-75.
[10]	Yatao YANG,Xinguang HAN,Jierun HUANG,Yang ZHAO. Bidirectional authentication key agreement protocol supporting identity’s privacy preservation based on RLWE [J]. Journal on Communications, 2019, 40(11): 180-186.
[11]	Yu FU, Yihan YU, Xiaoping WU. Differential privacy protection technology and its application in big data environment [J]. Journal on Communications, 2019, 40(10): 157-168.
[12]	Weihao LI,Sheng DING,Jiajie MENG,Hui LI. Spatio-temporal aware privacy-preserving scheme in LBS [J]. Journal on Communications, 2018, 39(5): 134-142.
[13]	Xiaoying ZHANG,Hui PENG,Hong CHEN. State-of-the-art survey of privacy-preserving data aggregation in wireless sensor networks [J]. Journal on Communications, 2018, 39(10): 130-142.
[14]	Qian ZHOU,Xiaolin QIN,Youwei DING. Preserving source-location privacy efficiently based on attack-perceiving in wireless sensor network [J]. Journal on Communications, 2018, 39(1): 101-116.
[15]	Zhuo-xiong PEI,Xing-hua LI,Hai LIU,Kai-yue LEI,Jian-feng MA,Hui LI. Anonymizing region construction scheme based on query range in location-based service privacy protection [J]. Journal on Communications, 2017, 38(9): 125-132.