摘要:
在基于区块链的群智感知系统中构建数据真值估计机制和用户激励机制受到了越来越多的关注.与传统的群智感知系统依赖一个集中平台来承载数据感知任务不同，该系统利用区块链分布式结构和操作透明不可抵赖的特性，使其具有更好的安全性和交互性.但是目前的研究总是独立分离设计数据真值估计机制和参与者激励机制，这导致2类机制在实际应用时往往具有局限性.针对这一问题，在综合考虑了数据真值估计精确度与用户激励后，提出了一类基于隐私保护数据真值估计的用户激励机制.该机制由2个模块组成，具有隐私保护的数据真值估计模块PATD和具有隐私保护的用户激励模块PFPI，这2个模块都是通过利用同态加密机制CKKS来构建的.由于数据采集设备精确度不够等原因，用户收集的数据往往具有噪声，因此PATD对用户提交的含有噪声的数据的加密结果进行计算，并将解密后的计算结果作为相应数据真值的估计.因为所用的数据均是加密的，所以可以保护用户数据隐私，同时，该机制还可以保证解密后的估计值具有较高的估计精度.此外，作为一种激励机制，PFPI满足真实性、个体合理性且具有较高的社会福利，同时利用CKKS保证用户在竞标过程中的竞价隐私安全.最后，进行了大量实验来验证所提的基于隐私保护数据真值估计的用户激励机制的各种特性.实验结果表明，该机制与最新方法相比具有更好的性能.
Abstract:
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.
百度学术
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