A Distributed k-Anonymity Protocol for Location Privacy

被引:0
|
作者
Zhong, Ge [1 ]
Hengartner, Urs [1 ]
机构
[1] Univ Waterloo, Cheriton Sch Comp Sci, Waterloo, ON N2L 3G1, Canada
关键词
D O I
暂无
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
To benefit from a location-based service, a person must reveal her location to the service. However, knowing the person's location might allow the service to re-identify the person. Location privacy based on k-anonymity addresses this threat by cloaking the person's location such that there are at least k - 1 other people within the cloaked area and by revealing only the cloaked area to a location-based service. Previous research has explored two ways of cloaking: First, have a central server that knows everybody's location determine the cloaked area. However, this server needs to be trusted by all users and is a single point of failure. Second, have users jointly determine the cloaked area. However, this approach requires that all users trust each other, which will likely not hold in practice. We propose a distributed approach that does not have these drawbacks. Our approach assumes that there are multiple servers, each deployed by a different organization. A user's location is known to only one of the servers (e.g., to her cellphone provider), so there is no single entity that knows everybody's location. With the help of cryptography, the servers and a user jointly determine whether the k-anonymity property holds for the user's area, without the servers learning any additional information, not even whether the property holds. A user learns whether the k-anonymity property is satisfied and no other information. The evaluation of our sample implementation shows that our distributed k-anonymity protocol is sufficiently fast to be practical. Moreover, our protocol integrates well with existing infrastructures for location-based services, as opposed to the previous research.
引用
收藏
页码:253 / 262
页数:10
相关论文
共 50 条
  • [41] Efficient Location Privacy-Preserving k-Anonymity Method Based on the Credible Chain
    Wang, Hui
    Huang, Haiping
    Qin, Yuxiang
    Wang, Yunqi
    Wu, Min
    ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2017, 6 (06)
  • [42] Enhancing Sink-Location Privacy in Wireless Sensor Networks through k-Anonymity
    Chai, Guofei
    Xu, Miao
    Xu, Wenyuan
    Lin, Zhiyun
    INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS, 2012,
  • [43] A Distributed Location Trusted Service Achieving k-Anonymity against the Global Adversary
    Buccafurri, Francesco
    De Angelis, Vincenzo
    Idone, Maria Francesca
    Labrini, Cecilia
    2021 22ND IEEE INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2021), 2021, : 133 - 138
  • [44] k-anonymity based location privacy protection method for location-based services in Internet of Thing
    Wang, Bo
    Guo, Yina
    Li, Hongtao
    Li, Zhiying
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (20):
  • [45] Protecting query privacy with differentially private k-anonymity in location-based services
    Jinbao Wang
    Zhipeng Cai
    Yingshu Li
    Donghua Yang
    Ji Li
    Hong Gao
    Personal and Ubiquitous Computing, 2018, 22 : 453 - 469
  • [46] Protecting query privacy with differentially private k-anonymity in location-based services
    Wang, Jinbao
    Cai, Zhipeng
    Li, Yingshu
    Yang, Donghua
    Li, Ji
    Gao, Hong
    PERSONAL AND UBIQUITOUS COMPUTING, 2018, 22 (03) : 453 - 469
  • [47] A Location Privacy Protection Algorithm Based on Double K-Anonymity in the Social Internet of Vehicles
    Xing, Ling
    Jia, Xiaofan
    Gao, Jianping
    Wu, Honghai
    IEEE COMMUNICATIONS LETTERS, 2021, 25 (10) : 3199 - 3203
  • [48] AnonTwist: Nearest Neighbor Querying with Both Location Privacy and K-anonymity for Mobile Users
    Wang, Song
    Wang, X. Sean
    MDM: 2009 10TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT, 2009, : 443 - 448
  • [49] Location Privacy Protection for the Internet of Things with Edge Computing Based on Clustering K-Anonymity
    Jiang, Nanlan
    Zhai, Yinan
    Wang, Yujun
    Yin, Xuesong
    Yang, Sai
    Xu, Pingping
    SENSORS, 2024, 24 (18)
  • [50] Differentially Private k-Anonymity: Achieving Query Privacy in Location-Based Services
    Wang, Jinbao
    Cai, Zhipeng
    Ai, Chunyu
    Yang, Donghua
    Gao, Hong
    Cheng, Xiuzhen
    2016 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS (IIKI), 2016, : 475 - 480