Ciphertext-policy attribute-based encryption (CP-ABE) is widely used in fine-grained access control to realize data security sharing. However, most of the existing CP-ABE access control schemes involve intermediary entities, which might suffer from a high trust-building cost, single point of failure and so on. Due to the decentralization and transparency of blockchain, some blockchain-based access control schemes are proposed to address these problems, but bring new challenges, such as policy and attribute privacy leakage. In this paper, we propose a new trustworthy secure ciphertext-policy and attribute hiding access control scheme based on blockchain, named TrustAccess, to achieve trustworthy access while guaranteeing the privacy of policy and attribute. For one thing, to make the existing hidden policy CP-ABE more efficient and scalable for blockchain, we propose an optimized hidden policy CP-ABE, named HP-CP-ABE, to ensure policy privacy while satisfying large universe access requirement. For another thing, we use ElGamal homomorphic encryption to ensure the attribute privacy during permission verification. Finally, we theoretically prove the effectiveness of our TrustAccess by security analysis from the aspect of blockchain operations and HP-CP-ABE. Comprehensive comparisons and extensive experiments are conducted to demonstrate the advantages of our TrustAccess.

Blockchain-based product traceability systems are receiving increasing attention from both industry and academia. Existing systems make full use of the traceability and non-modification characteristics of blockchain technology and realize the openness and transparency of product traceability information in the entire supply chain. However, existing systems do not consider government regulation, cannot protect enterprise sensitive private data effectively, and have performance bottlenecks. To address these problems, this paper proposes a product traceability scheme based on the permissioned blockchain within a double-layer framework. We introduce the double-layer framework and describe its advantages in detail. We also describe the smart contracts (chain code) in the double-layer framework. Finally, we test the performance of the proposed scheme through simulation experiments. The simulation results demonstrate the performance of nodes in the main layer, which is very important for consumers to obtain product traceability information, is optimized.

Deep learning has aroused a lot of attention and has been used successfully in many domains. Generally, the training of models requires large, representative datasets, which may be collected from a large number of users and contain sensitive information. The collected data would be stored and computed by service providers (SPs) or delegated to an untrusted cloud. Users can neither control how it will be used, nor realize what will be learned, which make the privacy issues prominent and severe. To solve the privacy issues, one of the most popular approaches is to encrypt their data. However, this technique inevitably leads to another challenge that how to train the model based on multi-key encrypted data. In this paper, we propose a novel privacy-preserving learning model, namely PDLM, to apply deep learning over the multi-key encrypted data. In PDLM, users contribute their encrypted data to SP to learn a specific model. We adopt an effective privacy-preserving calculation toolkit to achieve the training in a privacy-preserving manner. Finally, we conduct the analysis of PDLM in both theory and practice and the experimental results over two real-world datasets demonstrate that our PDLM can effectively and efficiently train the model in a privacy-preserving way.

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.

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.