Blockchain technology has attracted considerable attention due to the boom of cryptocurrencies and decentralized applications. Among them, the emerging blockchain-based crowdsourcing is a typical paradigm, which gets rid of centralized cloud-servers and leverages smart contracts to realize task recommendation and reward distribution. However, there are still two critical issues yet to be solved urgently. First, malicious evaluation from crowdsourcing requesters will result in honest workers not getting the rewards they deserve even if they have provided valuable solutions. Second, unfair evaluation and reward distribution can lead to low enthusiasm for work. Therefore, the above problems will seriously hinder the development of blockchain-based crowdsourcing platforms. In this paper, we propose a new blockchain-based crowdsourcing framework with enhanced trustworthiness and fairness, named TFCrowd. The core idea of TFCrowd is utilizing a smart contract of blockchain as a trusted authority to fairly evaluate contributions and allocate rewards. To this end, we devise a reputation-based evaluation mechanism to punish the requester who behaves as “false-reporting” and a Shapley value-based method to distribute rewards fairly. By using our proposed schemes, TFCrowd can prevent malicious requesters from making unfair comments and reward honest workers according to their contributions. Extensive simulations and the experiment results demonstrate that TFCrowd can protect the interests of workers and distribute rewards fairly.
Proof of work (PoW), the most popular consensus mechanism for Blockchain, requires ridiculously large amounts of energy but without any useful outcome beyond determining accounting rights among miners. To tackle the drawback of PoW, we propose a novel energy-recycling consensus algorithm, namely proof of federated learning (PoFL), where the energy originally wasted to solve difficult but meaningless puzzles in PoW is reinvested to federated learning. Federated learning and pooledming, a trend of PoW, have a natural fit in terms of organization structure. However, the separation between the data usufruct and ownership in Blockchain lead to data privacy leakage in model training and verification, deviating from the original intention of federal learning. To address the challenge, a reverse game-based data trading mechanism and a privacy-preserving model verification mechanism are proposed. The former can guard against training data leakage while the latter verifies the accuracy of a trained model with privacy preservation of the task requester's test data as well as the pool's submitted model. To the best of our knowledge, our paper is the first work to employ federal learning as the proof of work for Blockchain. Extensive simulations based on synthetic and real-world data demonstrate the effectiveness and efficiency of our proposed mechanisms.
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