A bidirectional reversible and multilevel location privacy protection method based on attribute encryption

被引:0
作者
Hu, Zhaowei [1 ]
Hu, Kaiyi [2 ]
Hasan, Milu Md Khaled [1 ]
机构
[1] Changzhou Univ, Sch Comp Sci & Artificial Intelligence, Changzhou, Peoples R China
[2] Changzhou Inst Educ Sci, Jr High Sch, Changzhou, Peoples R China
来源
PLOS ONE | 2024年 / 19卷 / 09期
关键词
SCHEME;
D O I
10.1371/journal.pone.0309990
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Various methods such as k-anonymity and differential privacy have been proposed to safeguard users' private information in the publication of location service data. However, these typically employ a rigid "all-or-nothing" privacy standard that fails to accommodate users' more nuanced and multi-level privacy-related needs. Data is irrecoverable once anonymized, leading to a permanent reduction in location data quality, in turn significantly diminishing data utility. In the paper, a novel, bidirectional and multi-layered location privacy protection method based on attribute encryption is proposed. This method offers layered, reversible, and fine-grained privacy safeguards. A hierarchical privacy protection scheme incorporates various layers of dummy information, using an access structure tree to encrypt identifiers for these dummies. Multi-level location privacy protection is achieved after adding varying amounts of dummy information at different hierarchical levels N. This allows for precise control over the de-anonymization process, where users may adjust the granularity of anonymized data based on their own trust levels for multi-level location privacy protection. This method includes an access policy which functions via an attribute encryption-based access control system, generating decryption keys for data identifiers according to user attributes, facilitating a reversible transformation between data anonymity and de-anonymity. The complexities associated with key generation, distribution, and management are thus markedly reduced. Experimental comparisons with existing methods demonstrate that the proposed method effectively balances service quality and location privacy, providing users with multi-level and reversible privacy protection services.
引用
收藏
页数:26
相关论文
共 27 条
  • [1] A hierarchical distributed trusted location service achieving location k-anonymity against the global observer
    Buccafurri, Francesco
    De Angelis, Vincenzo
    Idone, Maria Francesca
    Labrini, Cecilia
    [J]. COMPUTER NETWORKS, 2024, 243
  • [2] Trajectory Privacy Protection Based on Sensitive Stay Area Replacement in Publishing
    Ji, Yali
    Gui, Xiaolin
    Dai, Huijun
    An, Jian
    Zhu, Hongyi
    Peng, Zhenlong
    Lin, Xinyang
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2022, 2022
  • [3] Using location semantics to realize personalized road network location privacy protection
    Kuang, Li
    Wang, Yin
    Zheng, Xiaosen
    Huang, Lan
    Sheng, Yu
    [J]. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2020, 2020 (01)
  • [4] Reversible spatio-temporal perturbation for protecting location privacy
    Li, Chao
    Palanisamy, Balaji
    [J]. COMPUTER COMMUNICATIONS, 2019, 135 : 16 - 27
  • [5] ReverseCloak: A Reversible Multi-level Location Privacy Protection System
    Li, Chao
    Palanisamy, Balaji
    Kalaivanan, Aravind
    Raghunathan, Sriram
    [J]. 2017 IEEE 37TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS (ICDCS 2017), 2017, : 2521 - 2524
  • [6] Li Chao., 2015, Proceedings of the Internatinal Conference on Network and System Security, P449
  • [7] Cache-Based Privacy Protection Scheme for Continuous Location Query
    Liu, Zhenpeng
    Miao, Dewei
    Li, Ruilin
    Liu, Yi
    Li, Xiaofei
    [J]. ENTROPY, 2023, 25 (02)
  • [8] A Trajectory Privacy Protection Method Based on Random Sampling Differential Privacy
    Ma, Tinghuai
    Song, Fagen
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2021, 10 (07)
  • [9] LBS user location privacy protection scheme based on trajectory similarity
    Qian, Kun
    Li, Xiaohui
    [J]. SCIENTIFIC REPORTS, 2022, 12 (01)
  • [10] A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities
    Shen, Hua
    Wang, Yu
    Zhang, Mingwu
    [J]. SENSORS, 2023, 23 (24)