A Survey of Crowdsensing and Privacy Protection in Digital City

被引:11
作者
Cheng, Xu [1 ,2 ]
He, Bin [1 ,2 ]
Li, Gang [1 ,2 ]
Cheng, Bin [1 ,2 ]
机构
[1] Tongji Univ, Dept Control Sci & Engn, Shanghai 201804, Peoples R China
[2] Tongji Univ, Shanghai Res Inst Intelligent Autonomous Syst, Shanghai 201804, Peoples R China
基金
中国国家自然科学基金;
关键词
Crowdsensing; Privacy; Urban areas; Sensors; Task analysis; Data privacy; Mobile handsets; digital city; mobile devices; privacy protection; sensing tasks; PRESERVING DATA AGGREGATION; LOCATION-PRIVACY; INCENTIVE MECHANISM; TASK ALLOCATION; DATA-COLLECTION; CURRENT STATE; MOBILE; SCHEME; SMART; AGE;
D O I
10.1109/TCSS.2022.3204635
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The key pillar of developing digital city is the ubiquitous sensing of people and the environment. Crowdsensing requires a large number of users to participate in the collection of sensing data, and these data may carry sensitive information, such as identity and location related to the users or sensing object. If this information is eavesdropped, intercepted, and leaked, this may seriously harm the interests of individuals, organizations, and even countries. Therefore, from a privacy perspective, users may be reluctant to open data. While relying on mobile devices used by a large number of ordinary users as the basic sensing unit, it is necessary to include a variety of communication methods to realize the distribution of sensing tasks and to collect the sensing data. Then, to complete the complex crowdsensing tasks, it is important to ensure privacy security in the context of crowdsensing because it is a key problem. In this article, we comb through the development status of crowdsensing in the digital city, emphatically analyze the privacy protection in crowdsensing under the background of digital city, and qualitatively evaluate the existing privacy protection technologies for crowdsensing. Finally, this article presents research challenges and future directions that should be addressed to improve the performance of privacy protection technologies for crowdsensing systems.
引用
收藏
页码:3471 / 3487
页数:17
相关论文
共 125 条
[1]   Privacy and human behavior in the age of information [J].
Acquisti, Alessandro ;
Brandimarte, Laura ;
Loewenstein, George .
SCIENCE, 2015, 347 (6221) :509-514
[2]   TCNS: Node Selection With Privacy Protection in Crowdsensing Based on Twice Consensuses of Blockchain [J].
An, Jian ;
Yang, He ;
Gui, Xiaolin ;
Zhang, Wendong ;
Gui, Ruowei ;
Kang, Jingjing .
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2019, 16 (03) :1255-1267
[3]  
Benbrahim H., 2020, P 3 INT C SMART CIT, P311
[4]   SigSense: Mobile Crowdsensing Based Incentive Aware Geospatial Signal Monitoring for Base Station Installation Recommendation Using Mixed Reality Game [J].
Bhattacharya, Aakashjit ;
De, Debashis .
WIRELESS PERSONAL COMMUNICATIONS, 2022, 123 (03) :2863-2894
[5]  
Biagioni J., 2011, P 9 ACM C EMBEDDED N, P68
[6]  
Burke J. A, 2006, UCLA CTR EMBEDDED NE, P1
[7]  
Capponi A, 2019, IEEE COMMUN SURV TUT, V21, P2419, DOI [10.1109/COMST.2019.2914030, 10.1109/isscs.2019.8801767]
[8]   A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures [J].
Capponi, Andrea ;
Fiandrino, Claudio ;
Kliazovich, Dzmitry ;
Bouvry, Pascal ;
Giordano, Stefano .
IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING, 2017, 2 (01) :3-16
[9]   Private data aggregation with integrity assurance and fault tolerance for mobile crowd-sensing [J].
Chen, Jianwei ;
Ma, Huadong ;
Zhao, Dong .
WIRELESS NETWORKS, 2017, 23 (01) :131-144
[10]   A Pricing Approach Toward Incentive Mechanisms for Participant Mobile Crowdsensing in Edge Computing [J].
Chen, Xin ;
Tang, Chao ;
Li, Zhuo ;
Qi, Lianyong ;
Chen, Ying ;
Chen, Shuang .
MOBILE NETWORKS & APPLICATIONS, 2020, 25 (04) :1220-1232