Worker selection in crowdsensing plays an important role in the quality control of sensing services. The majority of existing studies on worker selection {\color{red}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 {\color{red}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 paper, we {\color{red}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 {\color{red}could} make the worker selection process trustworthy. To protect workers' reputation privacy in our TrustWorker, we {\color{red}adopted} a deterministic encryption algorithm to encrypt reputation values and then {\color{red}selected} the top N workers in the light of secret minimum heapsort scheme. Finally, we theoretically {\color{red}analyzed} the effectiveness and efficiency of our TrustWorker, and then {\color{red}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.

Federated learning (FL) is essentially a distributed machine learning paradigm that enables the joint training of a global model by aggregating gradients from participating clients without exchanging raw data. However, a malicious aggregation server may deliberately return designed results without any operation to save computation overhead, or even launch privacy inference attacks using crafted gradients. There are only a few schemes focusing on verifiable FL, and yet they cannot achieve collusion-resistant verification. In this paper, we propose the first Verifiable, Collusion-resistant, and Dynamic FL (VCD-FL) to tackle this issue. Specifically, we first optimize Lagrange interpolation by gradient grouping and compression for achieving efficient verifiability of FL. To protect clients’ data privacy against collusion attacks, we propose a lightweight commitment scheme using irreversible gradient transformation. By integrating the proposed efficient verification mechanism with the novel commitment scheme, our VCD-FL can detect whether or not the aggregation server is involved in collusion attacks. Moreover, considering that clients might go offline due to some reason such as network anomaly and client crash, we adopt the secret sharing technique to eliminate the effect of federation dynamics on FL. To the best of our knowledge, this is the first work to achieve collusion-resistant verification and collusion attack detection with supporting the correctness, privacy, and dynamics. Finally, we theoretically prove the effectiveness of our VCD-FL, make comprehensive comparisons, and conduct a series of experiments on MNIST dataset with MLP and CNN models. The theoretical proof and experimental analysis demonstrate that our VCD-FL is computationally efficient, robust against collusion attacks, and able to support the dynamics of FL.

Federated learning (FL), a cooperative distributed learning framework, has been employed in various intelligent Internet of Things (IoT) applications (e.g., smart health-care, smart home, and smart industry). However, there may be malicious devices in these IoT applications inferring other devices’ privacy or destroying the uploaded model parameters. Besides, due to the heterogeneity of IoT devices, it is difficult for the existing synchronized FL to effectively train models through non-identical independently distributed (non-IID) local data sets. To address these issues, we propose an asynchronous grouped federated learning framework (PAG-FL) for IoT, enabling multiple devices and the server to collaboratively and efficiently train models without revealing privacy. PAG-FL framework consists of an adaptive Rényi Differential Privacy-based privacy budget allocation (ARB) protocol and an asynchronous weight-based grouped update (AWGU) algorithm. In particular, our ARB protocol applies Rényi Differential Privacy and adaptively adjusts the privacy budget to obtain an efficient local model. The AWGU algorithm can defend against the poisoning attack on non-IID data set by weighing grouped local models to generate a global model. Meanwhile, it also realizes the asynchronous optimized update by adopting a lazy loading strategy. Theoretically, the proposed framework has a convergence guarantee and a privacy guarantee when training over the non-IID data set in an asynchronous FL. Our empirical experiments validate the effectiveness of the theoretical design and demonstrate the improved utility and robustness of PAG-FL in heterogeneous IoT.

The location information for tasks may expose sensitive information, which impedes the practical use of mobile crowdsensing in the industrial Internet. In this article, to our knowledge, we are the first to discuss the privacy protection of task locations and propose a codebook-based task allocation mechanism to protect it. Considering the cost of system utility caused by privacy protection technology, the tradeoff between local privacy and system utility is formalized a multiobjective optimization problem. The optimal solution is theoretically derived, and the optimal task allocation scheme is obtained. In addition, the selected allocation codebook (SAC) method is introduced to solve the problem of high computational resource consumption in the task allocation process and protect the task location privacy to some extent. The experimental results show that the SAC method sacrifices system utility but improves the privacy protection for task locations by 60% on average.

Mobile crowdsensing systems (MCSs) are widely used in data collection due to their flexible deployment and comprehensive coverage in many IoT scenarios (e.g., road condition monitoring). Recently, the difference between workers’ perception on location privacy has drawn researchers’ attention. The only privacy trading mechanism in MCSs has been designed, however, in a single auction and single-minded way. Realizing task requesters’ competition requirement and workers’ task preference variance, in this article, we are the first to propose a double MCS auction mechanism with a personalized location privacy incentive. Specifically, this article introduces the concept of privacy budget, allowing workers to decide how much location information to disclose to the platform to realize personalized location privacy protection. Besides, considering the heterogeneity of sensing tasks and the diversity of task selection, each worker is allowed to offer several bids for interested tasks and to perform a subset of tasks in a bid if wins. In addition, our auction mechanism enables the platform to select winning requesters and workers and achieve ideal sensing service accuracy. Extensive theoretical analysis and experiment results validate that the proposed mechanism satisfies budget balance, individual rationality, and 2-D-truthfulness.

The impact of fog-assisted Healthcare IoT (H-IoT) system is immense. The smart H-IoT equipments can upload healthcare information to fog nodes with low latency and high mobility. To facilitate secure interactions among three parties, including smart H-IoT equipments, fog nodes and a cloud server, over the public and insecure channels, a few authenticated key agreement (AKA) protocols are proposed. However, existing works are constructed based on expensive cryptographic primitives (e.g., bilinear pairing), which lead to high computation costs. Besides, the anonymity of H-IoT users is failed to be provided. To tackle these issues, an anonymous and lightweight three-party AKA protocol ALAKAP is proposed, which leverages an efficient cryptographic primitive (i.e., Chebyshev chaotic map operation) to generate a shared session key among three parties and achieve security (anonymity and other six properties) and efficiency simultaneously. It then formally proves the security of ALAKAP under the broadly accepted Burrows-Abadi-Needham (BAN) logic model and demonstrates how the proposed protocol satisfies the desired requirements in the fog-assisted H-IoT system. Finally, the performance of ALAKAP is validated by conducting the experiments on Amazon EC2 and Raspberry Pi. The results show that our work can achieve at least 44% higher improvement than the state-of-the-art works.