Research on Location Privacy Protection Methods for Mobile Users in 5G Environment

被引：0
|
作者：
Jiang H. [1 ]
Zeng J. [1 ]
Han K. [2 ]
Liu L. [2 ]
机构：
[1] School of Economy Management, Beijing University of Posts and Telecommunications, Beijing
[2] School of Electronics Engineering, Beijing University of Posts and Telecommunications, Beijing
来源：
Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology | 2021年 / 41卷 / 01期
关键词：
5G environment; Location privacy protection; Privacy protection algorithm;
D O I：
10.15918/j.tbit1001-0645.2020.139
中图分类号：
学科分类号：
摘要：
To solve the location privacy disclosure problem with location based service(LBS)applied frequently in 5G environment, the risks of mobile users' location privacy disclosure in 5G environment were analyzed, the existing privacy protection technologies were investigated, and three kinds of common privacy protection methods were summarized and compared. A new privacy protection method called fusion location privacy protection method was proposed, aiming at the new challenges of location privacy protection in 5G environment. By the combination of preliminary dimension reduction treatment, location privacy protection algorithm, and transmission encryption method, the method was arranged to reduce the risks during location dimension selecting, positioning and transmitting process without increasing the complexity. Simulation results indicate that the proposed method can perform well in mixed scenarios and be suitable for the dense, high frequency LBS in 5G environment. © 2021, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
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页码：84 / 92
页数：8
相关论文
共 18 条
  • [1] MA C Y T, YAU D K Y, Yip N K, Et al., Privacy vulnerability of published anonymors mobility traces, IEEE/ACM Transactions on Networking, 21, 3, pp. 720-733, (2013)
  • [2] ZHANG Xuejun, Gui Xiaolin, WU Zhongdong, Privacy preservation for location-based services: a survey, Journal of Software, 26, 9, pp. 2373-2395, (2015)
  • [3] KIM Hyeong-Il, KIM Hyeong-Jin, CHANG Jae-Woo, A kNN query processing algorithm using a tree index structure on the encrypted database, 2016 International Conference on Big Data and Smart Computing(BigComp), pp. 93-100, (2016)
  • [4] ZHOU Changli, CHEN Yonghong, TIAN Hui, Et al., Location privacy and query privacy preserving method for K-nearestneighbor query in road networks, Journal of Software, 31, 2, pp. 471-492, (2020)
  • [5] YAN Guanghui, LIU Ting, ZHANG Xuejun, Et al., Service similarity location k anonymity privacy protection scheme against background knowledge inference attacks, Journal of Xi'an Jiaotong University, 54, 1, pp. 8-18, (2020)
  • [6] GRUTESER M, GRUNWALD D., Anonymous usage of location-based services through spatial and temporal cloaking, International Conference on Mobile Systems, pp. 31-42, (2003)
  • [7] YI X, PAULET R, BERTINO E, Et al., Practical approximate K nearest neighbor queries with location and query privacy, IEEE Transactions on Knowledge Data Engineering, 28, 6, pp. 1546-1559, (2016)
  • [8] YIGITOGLU E, DAMIANI M L, ABUL O, Et al., Privacy-preserving sharing of sensitive semantic locations under road-network constraints, Proc.of the 13th IEEE Int'l Conf.on Mobile Data Management(MDM), pp. 186-195, (2012)
  • [9] LI Weihao, DING Sheng, MENG Jiajie, Et al., Spatio-temporal aware privacy-preserving scheme in LBS, Journal on Communications, 39, 5, pp. 134-142, (2018)
  • [10] PARATE A, CHIU M C, GANESAN D, Et al., Leveraging graphical models to improve accuracy and reduce privacy risks of mobile sensing, Proc.of the 11th Annual Int'l Conf.on Mobile System, Applications, and Services, pp. 83-96, (2013)
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