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.

Deep learning has been successfully applied to infrared and visible image fusion due to its powerful ability of feature representation. Existing most deep learning based infrared and visible image fusion methods mainly utilize pure convolution model or pure transformer model, which leads to that the fused image cannot preserve long-range dependencies (global context) and local features simultaneously. To this end, we propose a convolution-guided transformer framework for infrared and visible image fusion (CGTF), which aims to combine the local features of convolutional network and the long-range dependency features of transformer to produce satisfactory fused image. In CGTF, the local features are calculated by convolution feature extraction module, and then the local features are used to guide the transformer feature extraction module to capture the long-range dependencies of the image, which can overcome not only the lack of long-range dependencies that exists in convolutional fusion methods, but also the deficiency of local feature that exists in transformer models. Moreover, the convolution-guided transformer fusion framework can consider the inherent relationship of local feature and long-range dependencies due to the alternate use of convolution feature extraction module and transformer module. In addition, to strengthen local feature propagation, we employ dense connections among convolution feature extraction modules. Ablation experiments demonstrate the effectiveness of convolution-guided transformer fusion framework and loss function. We employ two datasets to compare our method with other nine methods, which includes three traditional methods, five deep learning based methods and one transformer based method. Qualitative and quantitative experiments demonstrate the advantages of our method.

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.

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.

Economic systems play pivotal roles in the metaverse. However, we have not yet found an overview that systematically introduces economic systems for the metaverse. Therefore, we review the state-of-the-art solutions, architectures, and systems related to economic systems. When investigating those state-of-the-art studies, we keep two questions in mind: (1) What is the framework of economic systems in the context of the metaverse? and (2) What activities would economic systems engage in the metaverse? This article aims to disclose insights into the economic systems that work for both the current and the future metaverse. To have a clear overview of the economic system framework, we mainly discuss the connections among three fundamental elements in the metaverse, i.e., digital creation, digital assets, and the digital trading market. After that, we elaborate on each topic of the proposed economic system framework. Those topics include incentive mechanisms, monetary systems, digital wallets, decentralized finance activities, and cross-platform interoperability for the metaverse. For each topic, we mainly discuss three questions: (a) the rationale of this topic, (b) why the metaverse needs this topic, and (c) how this topic will evolve in the metaverse. Through this overview, we wish readers can better understand what economic systems the metaverse needs and the insights behind the economic activities in the metaverse.