Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection were largely dependent on a trusted centralized server, which might suffer from single point of failure, the lack of transparency and so on. Some works recently proposed blockchain-based crowdsensing, which utilized reputation values stored on blockchains to select trusted workers. However, the transparency of blockchains enables attackers to effectively infer private information about workers by the disclosure of their reputation values. In this article, we proposed the TrustWorker, a trustworthy and privacy-preserving worker selection scheme for blockchain-based crowdsensing. By taking the advantages of blockchains such as decentralization, transparency and immutability, our TrustWorker could make the worker selection process trustworthy. To protect workers’ reputation privacy in our TrustWorker, we adopted a deterministic encryption algorithm to encrypt reputation values and then selected the top$N$workers in the light of secret minimum heapsort scheme. Finally, we theoretically analyzed the effectiveness and efficiency of our TrustWorker, and then conducted a series of experiments. The theoretical analysis and experiment results demonstrate that our TrustWorker can achieve trustworthy worker selection, while ensuring the workers’ privacy and the high quality of sensing services.

In this paper, we study the problem of image classification where training images are ambiguously annotated with multiple candidate labels, among which only one is correct but is not accessible during the training phase. Due to the adopted non-deep framework and improper disambiguation strategies, traditional approaches are usually short of the representation ability and discrimination ability, so their performances are still to be improved. To remedy these two shortcomings, this paper proposes a novel approach termed “Deep Discriminative CNN” (D2CNN) with temporal ensembling. Specifically, to improve the representation ability, we innovatively employ the deep convolutional neural networks for ambiguously-labeled image classification, in which the well-known ResNet is adopted as our backbone. To enhance the discrimination ability, we design an entropy-based regularizer to maximize the margin between the potentially correct label and the unlikely ones of each image. In addition, we utilize the temporally assembled predictions of different epochs to guide the training process so that the latent groundtruth label can be confidently highlighted. This is much superior to the traditional disambiguation operations which treat all candidate labels equally and identify the hidden groundtruth label via some heuristic ways. Thorough experimental results on multiple datasets firmly demonstrate the effectiveness of our proposed D2CNN when compared with other existing state-of-the-art approaches.