• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Shen Zhengchen, Zhang Qianli, Zhang Chaofan, Tang Xiangyu, Wang Jilong. Location Privacy Attack Based on Deep Learning[J]. Journal of Computer Research and Development, 2022, 59(2): 390-402. DOI: 10.7544/issn1000-1239.20200843
Citation: Shen Zhengchen, Zhang Qianli, Zhang Chaofan, Tang Xiangyu, Wang Jilong. Location Privacy Attack Based on Deep Learning[J]. Journal of Computer Research and Development, 2022, 59(2): 390-402. DOI: 10.7544/issn1000-1239.20200843

Location Privacy Attack Based on Deep Learning

Funds: This work was supported by the National Key Research and Development Program of China (2017YFB0503703).
More Information
  • Published Date: January 31, 2022
  • With the continuous development of location services, location privacy protection has become a hotspot in privacy protection research. At present, a series of location privacy protection schemes have been proposed, most of which are based on spatial disturbance technology. However, the existing research on location privacy protection has two problems: First of all, most of the location privacy protection schemes do not consider the complicated correlation between the trajectory points of a single trajectory when performing spatial disturbances, and they usually underestimate the risk of cracking desensitization trajectories; Secondly, there is a lack of quantitative measurement of the risk of cracking the desensitization trajectory. Although differential privacy has made considerable efforts in this regard, the existence of complex relationships makes the model may not be able to objectively describe the degree of privacy protection. If the cracking risk of data after privacy protection cannot be quantified, a quantitative evaluation index cannot be established for the privacy protection scheme. Therefore, first of all, the location information with the association relationship is used to attack the desensitization trajectory. Specifically, the Markov attack algorithms using simple association relationships and the deep neural network attack algorithms using complex association relationships are designed in this paper. Secondly, the cracking risk of desensitization trajectory is quantified, and a quantitative evaluation scheme is established to evaluate the threat degree of attack algorithm to privacy protection scheme. Finally, these two kinds of attack algorithms are used to attack Geo-Indistinguishability privacy protection scheme, and the attack effect is evaluated. The results show that Geo-Indistinguishability privacy protection scheme can resist the attack of the Markov attack algorithm, but can not resist the attack of deep neural network attack algorithm.
  • Related Articles

    [1]Deng Qingyong, Zuo Qinghua, Li Zhetao, Wang En, Guo Bin. Privacy-Preserving Bilateral Reputation Evaluation in Blockchain Based Crowdsensing[J]. Journal of Computer Research and Development, 2024, 61(11): 2681-2692. DOI: 10.7544/issn1000-1239.202440302
    [2]Lu Feng, Li Wei, Gu Lin, Liu Shuai, Wang Runheng, Ren Yufei, Dai Xiaohai, Liao Xiaofei, Jin Hai. Selection of Reputable Medical Participants Based on an Iterative Collaborative Learning Framework[J]. Journal of Computer Research and Development, 2024, 61(9): 2347-2363. DOI: 10.7544/issn1000-1239.202330270
    [3]Hu Jianli, Zhou Bin, Wu Quanyuan, Li Xiaohua. A Reputation Based Attack-Resistant Distributed Trust Management Model for P2P Networks[J]. Journal of Computer Research and Development, 2011, 48(12): 2235-2241.
    [4]Ma Shouming, Wang Ruchuan, Ye Ning. Secure Data Aggregation Algorithm Based on Reputations Set Pair Analysis in Wireless Sensor Networks[J]. Journal of Computer Research and Development, 2011, 48(9): 1652-1658.
    [5]Feng Jingyu, Zhang Yuqing, Chen Shenlong, Fu Anmin. GoodRep Attack and Defense in P2P Reputation Systems[J]. Journal of Computer Research and Development, 2011, 48(8): 1473-1480.
    [6]Zhao Xiang, Huang Houkuan, Dong Xingye, and He Lijian. A Trust and Reputation System Model for Open Multi-Agent System[J]. Journal of Computer Research and Development, 2009, 46(9): 1480-1487.
    [7]Luo Junhai and Fan Mingyu. Research on Trust Model Based on Game Theory in Mobile Ad-Hoc Networks[J]. Journal of Computer Research and Development, 2008, 45(10): 1704-1710.
    [8]He Lijian, Huang Houkuan, Zhang Wei. A Survey of Trust and Reputation Systems in Multi Agent Systems[J]. Journal of Computer Research and Development, 2008, 45(7).
    [9]Jin Yu, Gu Zhimin, and Ban Zhijie. A New Reputation Management Mechanism Based on Bi-Ratings in Peer-to-Peer Systems[J]. Journal of Computer Research and Development, 2008, 45(6).
    [10]Chen Feifei and Gui Xiaolin. Research on Dynamic Trust-Level Evaluation Mechanism Based on Machine Learning[J]. Journal of Computer Research and Development, 2007, 44(2): 223-229.
  • Cited by

    Periodical cited type(24)

    1. 许炜,李卓卓,方向阳. 多向度的数据分类分级:目标、逻辑与路径. 图书情报工作. 2025(01): 68-79 .
    2. 刘怀骏,徐劲松. 基于区块链和代理重加密的快递出海数据共享方案. 物流科技. 2025(03): 82-86 .
    3. 白久君,陈雪波,李大明,刘锐. 构建数字化未来:算网融合的战略应用与研究. 广播电视网络. 2025(02): 28-31 .
    4. 任静,李筱永,梁恒瑜,赵依凡,吴佼玥. 脑科学视角下经颅磁刺激治疗精神障碍的法律问题及对策研究. 中国全科医学. 2024(08): 1015-1020 .
    5. 薛俊伟,吴凯,周静. 耳机式物联网血氧监护系统的设计. 中国医学物理学杂志. 2024(01): 60-65 .
    6. 杨斌,王正阳,程梓航,赵慧英,王鑫,管宇,程新洲. 基于扩散模型生成数据重构的客户流失预测. 计算机研究与发展. 2024(02): 324-337 . 本站查看
    7. 李敏,肖迪,陈律君. 兼顾通信效率与效用的自适应高斯差分隐私个性化联邦学习. 计算机学报. 2024(04): 924-946 .
    8. 刘立. 大数据技术在中职计算机教学应用初探. 科技风. 2024(12): 64-66+167 .
    9. 孔庆苹. 大数据环境下物联网设备数据隐私保护研究. 无线互联科技. 2024(07): 116-118 .
    10. 徐帅. 数据隐私保护与法律责任:新形势下的挑战与应对. 法制博览. 2024(14): 95-97 .
    11. 张海霞. 安全路由协议综合交互信任评价及性能分析. 山西电子技术. 2024(03): 69-70+90 .
    12. 张世涛,祁舒慧. 社交媒体数据分析在市场审计中的运用. 赤峰学院学报(自然科学版). 2024(07): 30-32 .
    13. 张国业,郎雅婧. 科技支撑区域工业治理能力提升路向选择及发展布局. 现代工业经济和信息化. 2024(10): 246-248 .
    14. 李卓卓,刘子轶. 从分野到融合:多学科视角下的数据跨境研究综述. 情报杂志. 2024(12): 198-207 .
    15. 蒋雷,朱婷婷,汤海林. 大数据背景下塑料加工行业的数据安全与隐私保护. 塑料助剂. 2024(06): 78-82 .
    16. 苗权,张弛,房硕,刘季平. 我国数据跨境流动管理的创新实践和思考. 互联网天地. 2023(03): 49-52 .
    17. 冯凡. 大数据分析技术下的隐私保护. 数字通信世界. 2023(03): 142-145 .
    18. 赵静. 基于区块链技术及数据挖掘技术推进数字经济发展. 科技资讯. 2023(15): 36-39 .
    19. 王鹏涛,徐润婕. AIGC介入知识生产下学术出版信任机制的重构研究. 图书情报知识. 2023(05): 87-96 .
    20. 赵尔波,苏玉成,黄少远. 医院部署GCP远程监查的多级安全防护设计与实践. 中国卫生信息管理杂志. 2023(05): 709-714 .
    21. 张铠,汪希,黄晋. 基于混沌技术的多域物联网敏感数据安全传输方法. 信息与电脑(理论版). 2023(16): 232-234 .
    22. 王大阜,王静,石宇凯,邓志文,贾志勇. 基于深度迁移学习的图像隐私目标检测研究. 图学学报. 2023(06): 1112-1120 .
    23. 郭赟赟,于浩. 突破语言障碍:ChatGPT在多语言教育中的作用与影响. 郑州师范教育. 2023(06): 48-53 .
    24. 姚莉娟,廖冬琴. 基于隐私保护的高校大数据挖掘平台设计. 无线互联科技. 2023(23): 50-54 .

    Other cited types(28)

Catalog

    Article views (589) PDF downloads (312) Cited by(52)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return