Blockchain technology presents significant security challenges despite its transformative impact on digital transactions and decentralized data management. Key vulnerabilities include insecure smart contract execution, data privacy risks on transparent ledgers, and susceptibility of certain consensus mechanisms to attacks. Trusted Execution Environments (TEEs) offer a robust hardware-based solution to these critical issues. By providing isolated execution spaces, TEEs safeguard code and data confidentiality and integrity, thereby fundamentally strengthening blockchain security. This paper presents a comprehensive analysis of TEEs in blockchain technology. First, we analyze the challenges inherent in blockchain systems and demonstrate the advantages of TEEs over current methods. A detailed analysis of TEE properties, variants, and evolution in the blockchain field is provided. Additionally, we explore innovative TEE-based solutions across three key application domains: consensus mechanism optimization, confidential computation and execution, and payment networks and financial applications. Furthermore, we propose a research agenda addressing current challenges such as vulnerabilities to side-channel attacks and dependencies on hardware trust assumptions. Finally, we propose five critical directions for future TEE-blockchain integration: enhancement of security and privacy protection with particular attention to the Trusted Computing Base (TCB) minimization, performance optimization through hardware architecture advancement, trust model refinement to reduce centralization, expansion of application scenarios through interdisciplinary collaboration, and development of cross-chain interoperability standards. Our work contributes to blockchain security knowledge and provides a roadmap for researchers and practitioners in this rapidly evolving field.

Federated Learning (FL) has emerged as a promising approach for collaborative model training without sharing private data. However, privacy concerns regarding information exchanged during FL have received significant research attention.Gradient Inversion Attacks (GIAs)have been proposed to reconstruct the private data retained by local clients from the exchanged gradients. While recovering private data, the data dimensions and the model complexity increase, which thwart data reconstruction by GIAs. Existing methods adopt prior knowledge about private data to overcome those challenges. In this article, we first observe that GIAs with gradients from a single iteration fail to reconstruct private data due to insufficient dimensions of leaked gradients, complex model architectures, and invalid gradient information. We investigate a Temporal Gradient Inversion Attack with a Robust Optimization framework, called TGIAs-RO, which recovers private data without any prior knowledge by leveraging multiple temporal gradients. To eliminate the negative impacts of outliers, e.g., invalid gradients for collaborative optimization, robust statistics are proposed. Theoretical guarantees on the recovery performance and robustness of TGIAs-RO against invalid gradients are also provided. Extensive empirical results on MNIST, CIFAR10, ImageNet and Reuters 21578 datasets show that the proposed TGIAs-RO with 10 temporal gradients improves reconstruction performance compared to state-of-the-art methods, even for large batch sizes (up to 128), complex models like ResNet18, and large datasets like ImageNet (224× 224pixels). Furthermore, the proposed attack method inspires further exploration of privacy-preserving methods in the context of FL.

Vehicle edge computing (VEC) leverages the computational and communication resources available from vehicles and mobile edge computing (MEC) servers to provide computing services for mobile vehicles. However, traditional task offloading and resource allocation methods based on deep reinforcement learning (DRL) often ignore the latent relationships between vehicles and MEC servers. This oversight results in a lack of robustness in highly mobile and complex environments. Therefore, this paper proposes a distributed dynamic task offloading and resource allocation (DDTORA) strategy in the context of vehicle-assisted multi-vehicle VEC scenarios. DDTORA aims to utilize idle computational resources of vehicles and find optimal task offloading and resource allocation schemes to minimize the weighted summation of latency and energy consumption for all tasks. To find the optimal solution, we propose a graph attention network based multi-agent deep reinforcement learning (GAMDRL) algorithm for distributed task offloading and resource allocation. Numerical simulations demonstrate that DDTORA converges faster than the benchmark algorithms, significantly reducing latency and energy consumption by 25.44% to 34.19%.

Federated learning (FL) is an efficient decentralized machine learning methodology for processing non-independent and identically distributed (non-IID) data due to geographical and temporal distribution differences. Non-IID data generally indicates substantial disparities in data distribution and features among clients. This assumption is completely different from the conventional assumption of independent and identically distributed (IID) data in which all clients’ data originates from the same distribution. There are many factors that affect the features of non-IID data, such as user preferences, data collection methods, and client characteristics. The factors of data distribution, category proportions, and feature representation also affect the statistical properties of non-IID data. This paper conducts an in-depth exploration of FL with the consideration of diverse features and statistical properties of non-IID data. Specifically, we first discuss the impact of non-IID data on communication efficiency, model convergence, and FL accuracy. The presence of non-IID data leads to increased communication overhead, imbalanced class distribution, and uneven local model updates. All of these affect FL convergence and performance. Then, we present the latest advanced techniques, such as data partitioning/sharing, client selection, differential privacy, and secure aggregation [1], which are used to address the challenges posed by non-IID data in terms of communication efficiency and privacy protection. Furthermore, we show the emerging applications and use cases of FL with non-IID data in various domains, such as healthcare, IoT, and edge computing. Overall, this survey provides a comprehensive understanding of FL with non-IID data, including the challenges, advancements, and practical applications in different areas.

Digital twin (DT) is a technology that precisely replicates physical entities and seamlessly connects physical entities with virtual counterparts, which facilitates precise understanding, optimization, and decision-making. DT network (DTN) can be regarded as an information-sharing network, comprising a constellation of interconnected DT nodes. This survey provides an in-depth exploration of the concepts and potential of DTN, with a particular focus on the role of machine and deep learning in improving the efficiency of DTN systems, including anomaly monitoring, system state estimation, resource allocation, task offloading, model optimization, and security and privacy protection. Incorporating machine and deep learning into DTN stands to revolutionize industries by enabling the extraction of critical insights, enhancing anomaly detection capabilities, refining the accuracy of predictive models, and optimizing the allocation of resources. Finally, we discuss the challenges and future research directions in the application of machine and deep learning in DTN.

Summary With the development and popularization of Internet technology, network security has become the focus of attention. Vulnerabilities and back doors are regarded as two of the main reasons of network security problems. Dynamic heterogeneous redundant (DHR) architecture is a typical framework of cyberspace mimic defense. It can make use of untrusted hardware and software components to construct a high reliable and high security information system. In this article, a mathematical model is set up for the DHR architecture, and the system security is characterized by vulnerability consistency rate and success rate of system attack. The antiattack ability of the DHR system is analyzed using the mathematical model.

Blockchain is widely used in encrypted currency, Internet of Things (IoT), supply chain finance, data sharing, and other fields. However, there are security problems in blockchains to varying degrees. As an important component of blockchain, hash function has relatively low computational efficiency. Therefore, this paper proposes a new scheme to optimize the blockchain hashing algorithm based on PRCA (Proactive Reconfigurable Computing Architecture). In order to improve the calculation performance of hashing function, the paper realizes the pipeline hashing algorithm and optimizes the efficiency of communication facilities and network data transmission by combining blockchains with mimic computers. Meanwhile, to ensure the security of data information, this paper chooses lightweight hashing algorithm to do multiple hashing and transforms the hash algorithm structure as well. The experimental results show that the scheme given in the paper not only improves the security of blockchains but also improves the efficiency of data processing.

As a recently proposed public key primitive, attribute-base encryption(ABE) divided into Ciphertext-policy ABE (CP-ABE) and Key-policy ABE (KP-ABE) is a highly promising tool for the management and protection of data. And Blockchain, as one of the core technologies of Bitcoin that is the most representative cryptocurrency, has received extensive attentions recently. Supervision and privacy protection are two difficulties in blockchain. In this paper, a new scheme combining blockchain and accountable CP-ABE is proposed. In this scheme, each change of the data is recorded on the blockchain. And the different permissions of access are realized through ABE. If a malicious user shares a decryption key illegally, it will be considered illegal. Similarly, if the authority generates a decryption key for any unauthorized user, it also will be considered illegal. This scheme allows any third party to publicly verify the identity of a decryption key. And it is achievable for an auditor to publicly audit whether a malicious user or the authority should be responsible for an exposed decryption key, and the key abuser cannot deny it. At last this scheme is applied to the management of electronic documents.

With the rise of Bitcoin, blockchain which is the core technology of Bitcoin has received increasing attention. Privacy preserving and performance on blockchain are two research points in academia and business, but there are still some unresolved issues in both respects. An aggregate signature scheme is a digital signature that supports making signatures on many different messages generated by many different users. Using aggregate signature, the size of the signature could be shortened by compressing multiple signatures into a single signature. In this paper, a new signature scheme for transactions on blockchain based on the aggregate signature was proposed. It was worth noting that elliptic curve discrete logarithm problem and bilinear maps played major roles in our signature scheme. And the security properties of our signature scheme were proved. In our signature scheme, the amount will be hidden especially in the transactions which contain multiple inputs and outputs. Additionally, the size of the signature on transaction is constant regardless of the number of inputs and outputs that the transaction contains, which can improve the performance of signature. Finally, we gave an application scenario for our signature scheme which aims to achieve the transactions of big data on blockchain.