A location data protection protocol based on differential privacy

被引:2
作者
Guo, Ping [1 ]
Ye, Baopeng [2 ]
Chen, Yuling [1 ]
Li, Tao [1 ]
Yang, Yixian [3 ]
Qian, Xiaobin [4 ]
机构
[1] Guizhou Univ, Coll Comp Sci & Technol, State Key Lab Publ Big Data, Guiyang, Peoples R China
[2] Informat Technol Innovat Serv Ctr Guizhou Prov, Guiyang, Peoples R China
[3] Beijing Univ Posts & Telecommnuicat, Sch Cyberspace Secur, Beijing, Peoples R China
[4] Guizhou CoVis Sci & Technol Co Ltd, Guiyang, Peoples R China
来源
2021 IEEE INTL CONF ON DEPENDABLE, AUTONOMIC AND SECURE COMPUTING, INTL CONF ON PERVASIVE INTELLIGENCE AND COMPUTING, INTL CONF ON CLOUD AND BIG DATA COMPUTING, INTL CONF ON CYBER SCIENCE AND TECHNOLOGY CONGRESS DASC/PICOM/CBDCOM/CYBERSCITECH 2021 | 2021年
基金
中国国家自然科学基金;
关键词
Location-based services; Smart contract; Differential; privacy; Data availability; Privacy protection;
D O I
10.1109/DASC-PICom-CBDCom-CyberSciTech52372.2021.00060
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Location-based services (LBS) is geo-location-based a mobile information service based on spatial location that adopts wireless positioning, GIS, Internet, wireless communication, database and other related technologies. which brings both convenience and vulnerability. With the increasing of scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility. To solve the revealing problems in LBS, in this paper, we first design an algorithm of the best-assisted user selection (BaUS) for constructing anonymity sets. Next, we contract a smart contract to evaluate reputation, to ensure the honesty of participants. And then a differential privacy protection protocol is proposed. The theoretical analysis and experiments show that the proposed protocol can resist background knowledge attacks effectively. Meanwhile, our protocol can improve data availability. Particularly, it realizes usercontrollable privacy protection which enhances privacy preservation and better security.
引用
收藏
页码:306 / 311
页数:6
相关论文
共 24 条
[11]   Is semi-selfish mining available without being detected? [J].
Li, Tao ;
Wang, Zhaojie ;
Chen, Yuling ;
Li, Chunmei ;
Jia, Yanling ;
Yang, Yixian .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2022, 37 (12) :10576-10597
[12]   Semi-selfish mining based on hidden Markov decision process [J].
Li, Tao ;
Wang, Zhaojie ;
Yang, Guoyu ;
Cui, Yang ;
Chen, Yuling ;
Yu, Xiaomei .
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, 2021, 36 (07) :3596-3612
[13]   Mechanism design via differential privacy [J].
McSherry, Frank ;
Talwar, Kunal .
48TH ANNUAL IEEE SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, PROCEEDINGS, 2007, :94-103
[14]   An anonymous entropy-based location privacy protection scheme in mobile social networks [J].
Ni, Lina ;
Tian, Fulong ;
Ni, Qinghang ;
Yan, Yan ;
Zhang, Jinquan .
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2019, 2019 (1)
[15]   DP-MCDBSCAN: Differential Privacy Preserving Multi-Core DBSCAN Clustering for Network User Data [J].
Ni, Lina ;
Li, Chao ;
Wang, Xiao ;
Jiang, Honglu ;
Yu, Jiguo .
IEEE ACCESS, 2018, 6 :21053-21063
[16]  
[欧阳丽炜 Ouyang Liwei], 2019, [自动化学报, Acta Automatica Sinica], V45, P445
[17]  
Pan X., 2017, LOCATION BIG DATA PR, P1
[18]   ESPADE: An Efficient and Semantically Secure Shortest Path Discovery for Outsourced Location-Based Services [J].
Samanthula, Bharath K. ;
Karthikeyan, Divya ;
Dong, Boxiang ;
Kumari, K. Anitha .
CRYPTOGRAPHY, 2020, 4 (04) :1-21
[19]   Enabling Smart Anonymity Scheme for Security Collaborative Enhancement in Location-Based Services [J].
Wu, Hongchen ;
Li, Mingyang ;
Zhang, Huaxiang .
IEEE ACCESS, 2019, 7 :50031-50040
[20]   Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT [J].
Xu, Xiaolong ;
He, Chengxun ;
Xu, Zhanyang ;
Qi, Lianyong ;
Wan, Shaohua ;
Bhuiyan, Md Zakirul Alam .
IEEE INTERNET OF THINGS JOURNAL, 2020, 7 (04) :2622-2629