TY - JOUR
T1 - Enabling data trustworthiness and user privacy in mobile crowdsensing
AU - Wu, Haiqin
AU - Wang, Liangmin
AU - Xue, Guoliang
AU - Tang, Jian
AU - Yang, Dejun
N1 - Funding Information:
Manuscript received June 23, 2019; accepted September 17, 2019; approved by IEEE/ACM TRANSACTIONS ON NETWORKING Editor K. Ren. Date of publication October 28, 2019; date of current version December 17, 2019. This work was supported in part by the National Science Foundation (NSF) under Grant 1717197, Grant 1717315, and Grant 1525920, in part by the National Natural Science Foundation of China under Grant U1736216 and Grant 61702233, in part by the National Key Research and Development Program under Grant 2017YFB1400703, and in part by the Graduate student innovation projects of Jiangsu province under Grant KYCX17_1810. The information reported here does not reflect the the policy of the funding agencies. The work of H. Wu was supported in part by the Chinese Scholarship Council under Grant 201708320241. (Corresponding authors: Guoliang Xue; Liangmin Wang.) H. Wu and L. Wang are with the Department of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China (e-mail: haiqinwu02@gmail.com; wanglm@ujs.edu.cn).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Ubiquitous mobile devices with rich sensors and advanced communication capabilities have given rise to mobile crowdsensing systems. The diverse reliabilities of mobile users and the openness of sensing paradigms raise concerns for data trustworthiness, user privacy, and incentive provision. Instead of considering these issues as isolated modules in most existing researches, we comprehensively capture both conflict and inner-relationship among them. In this paper, we propose a holistic solution for trustworthy and privacy-aware mobile crowdsensing with no need of a trusted third party. Specifically, leveraging cryptographic technologies, we devise a series of protocols to enable benign users to request tasks, contribute their data, and earn rewards anonymously without any data linkability. Meanwhile, an anonymous trust/reputation model is seamlessly integrated into our scheme, which acts as reference for our fair incentive design, and provides evidence to detect malicious users who degrade the data trustworthiness. Particularly, we first propose the idea of limiting the number of issued pseudonyms which serves to efficiently tackle the anonymity abuse issue. Security analysis demonstrates that our proposed scheme achieves stronger security with resilience against possible collusion attacks. Extensive simulations are presented which demonstrate the efficiency and practicality of our scheme.
AB - Ubiquitous mobile devices with rich sensors and advanced communication capabilities have given rise to mobile crowdsensing systems. The diverse reliabilities of mobile users and the openness of sensing paradigms raise concerns for data trustworthiness, user privacy, and incentive provision. Instead of considering these issues as isolated modules in most existing researches, we comprehensively capture both conflict and inner-relationship among them. In this paper, we propose a holistic solution for trustworthy and privacy-aware mobile crowdsensing with no need of a trusted third party. Specifically, leveraging cryptographic technologies, we devise a series of protocols to enable benign users to request tasks, contribute their data, and earn rewards anonymously without any data linkability. Meanwhile, an anonymous trust/reputation model is seamlessly integrated into our scheme, which acts as reference for our fair incentive design, and provides evidence to detect malicious users who degrade the data trustworthiness. Particularly, we first propose the idea of limiting the number of issued pseudonyms which serves to efficiently tackle the anonymity abuse issue. Security analysis demonstrates that our proposed scheme achieves stronger security with resilience against possible collusion attacks. Extensive simulations are presented which demonstrate the efficiency and practicality of our scheme.
KW - Mobile crowdsensing
KW - data trustworthiness
KW - incentive fairness
KW - user privacy
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U2 - 10.1109/TNET.2019.2944984
DO - 10.1109/TNET.2019.2944984
M3 - Article
AN - SCOPUS:85077307204
SN - 1063-6692
VL - 27
SP - 2294
EP - 2307
JO - IEEE/ACM Transactions on Networking
JF - IEEE/ACM Transactions on Networking
IS - 6
M1 - 8884665
ER -