Incentive Mechanism Based on Truth Estimation of Private Data for Blockchain- Based Mobile Crowdsensing

被引：0
作者：
Ying C. [1 ]
Xia F. [1 ]
Li J. [1 ,2 ,3 ]
Si X. [1 ,2 ,3 ]
Luo Y. [1 ,2 ,3 ]
机构：
[1] Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai
[2] Blockchain Research Center, Shanghai Jiao Tong University, Shanghai
[3] Wuxi Blockchain Advanced Research Center, Wuxi
来源：
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | 2022年 / 59卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Blockchain; Data collection; Incentive mechanism; Mobile crowdsensing; Privacy protection; Truth estimation;
D O I：
10.7544/issn1000-1239.20220493
中图分类号：
学科分类号：
摘要：
Recently, building truth estimation mechanism and participant incentive mechanism upon blockchain-based mobile crowd sensing systems attracts more and more attention. Unlike the traditional mobile crowd sensing system that relies on a centralized platform to host the sensing tasks, due to its decentralized structure, transparent operation and immutability nature, such a system built upon the blockchain is more safe and more interactive. However, the existing researches separately focus on building truth estimation mechanism and participant incentive mechanism, which may lead to the performance limitation in practice. Therefore, in this paper, we propose a participant incentive mechanism based on truth estimation of privacy-preserving data for blockchain-based mobile crowd sensing systems. In fact, it consists of two procedures, the privacy-aware truth estimation procedure (PATD) and the privacy-friendly participant incentive procedure (PFPI), both of which are built by applying Cheon, Kim, Kim, and Song's homomorphic encryption mechanism (CKKS). Due to the low accuracy of data collection devices, the collected data usually mixes with some noise. The collectors encrypt their noisy data. Then PATD utilizes the encrypted data submitted by the collectors to do some calculations and regards the corresponding decrypted result as the truth estimation. The privacy of submitted data can be protected since the data for truth estimation is encrypted by utilizing CKKS. It can also guarantee that the decrypted truth estimation has the high accuracy. Additionally, PFPI can attract more participants by satisfying the truthfulness and individual rationality, and also achieve a high social welfare. The privacy of participants' bids is protected by utilizing CKKS. Finally, numerous experiments are conducted to validate the desirable properties of our proposed mechanism, where the results show that compared with the state-of-the-art approaches, it has better performance. © 2022, Science Press. All right reserved.
引用
收藏
页码：2212 / 2232
页数：20
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