consensus protocols and peer-provided proofs are the necessary components used to establish trust in blockchain systems. Verifiable proofs that guarantee an elapsed time based on security components have emerged as alternatives to energy-intensive Proof of Work (PoW). These proofs are well-suited for embedding consensus protocols running on resource-constrained devices. This paper proposes Trust@TEE.TIME, an experimental platform designed to instrument and characterize several proof mechanisms within embedded devices. Our platform is proof-agnostic, allowing it to accommodate various proof generation mechanisms without modifications to the underlying hardware or architecture. It comprises Systems on Module featuring an ARM Cortex-A7 processor with a Trusted Execution Environment (TEE) and a Trusted Platform Module (TPM). These components are collaboratively used to ensure secure proof generation. This paper is an extension of a previous paper which introduced Proof of Hardware Time (PoHT). It provides a more detailed description of the experimental platform and a comparison of power consumption and time delay with different average elapsed time between blocks. In addition, we show that PoHT achieves an average power reduction factor of 7 to 117 compared to PoW.

Blockchain technology establishes trust among participants through technical means. However, some malicious nodes may compromise this trust through short-range reorganization attacks for their interest. This paper develops an agent-based model to systematically analyze Proof-of-Stake short-range reorganization attacks, where three types of agents interact through distributed consensus mechanisms with ex-ante, fine-grained, and ex-post reorganization attack strategies. Through rigorous simulation of agent decision-making dynamics, we identify that: (1) Compared with ex-ante reorganization, the ratio of malicious nodes required for ex-post reorganization is much larger. (2) Increasing the node number increases the difficulty of ex-ante and ex-post reorganization. (3) The number of nodes affects ex-post reorganization attacks more significantly than ex-ante attacks. (4) Fine-grained reorganization significantly reduces attack difficulty

This work presents SOvNet, a blockchain-agnostic hybrid solution that combines the privacy of proprietary systems with the resilience of public blockchains. SOvNet is a private network that publishes cryptographic fingerprints of its data on public blockchains, while aiming to simplify the management of personal data and transactions. The network is maintained by a group of nodes that updates the system status and provides cryptographic proofs of data processing to the network users. Designed for governments and enterprises, SOvNet supports services such as document management, real-world asset digitalisation, and digital payments. It also supports communication between SOvNet instances with minimal user-side overhead. The user interface to interact with SOvNet implements only lightweight operations, allowing for deployment on personal devices, including laptops and smartphones.

random beacons are crucial components in blockchain consensus, secure multiparty computation, and decentralized applications, providing high-quality randomness for these applications. However, random beacon services operated by a single organization face centralization issues and cannot be fully trusted by mission-critical applications due to possible breaches and collusion. Asynchronous distributed random beacon protocols are proposed as a promising alternative to such centralized services, since they can generate high-quality randomnesses that are unbiased and unpredictable for critical applications in the adversarial asynchronous Internet. However, they either suffer from expensive communication overhead or lack accommodation for efficient dynamic participation. To address these issues, we propose a practical asynchronous random beacon protocol that can be efficiently reconfigured to support rotations of participating nodes, reducing the reconfiguration’s communication complexity from O(λn3) to O(λ κn2), where λ is the cryptography security parameter, n is the size of nodes in the network, and κ is the small size of a any-trust sub-committee (which approximates a constant number about several dozens). We also demonstrate the performance and security of our scheme through thorough analysis and extensive experiments.

The rapid growth of decentralized finance (DeFi) has provided numerous benefits, but it has also presented significant economic security challenges. One of the most critical issues is Maximum Extractable Value (MEV). MEV refers to the opportunities for miners or validators to earn additional profits by altering the order of transactions. However, current MEV detection methods have notable limitations. These include poor adaptability of algorithms, the vastness of the search space, and the inefficiency of methods that rely on traditional heuristic approaches. To overcome these challenges, we introduces a reinforcement learning-based MEV optimization system for blockchain—RL-BES (Reinforcement Learning for Blockchain Economic Security). This system employs two deep reinforcement learning networks to optimize transaction ordering and template parameters, integrated with Monte Carlo Tree Search (MCTS) for effective path exploration. Furthermore, we presents a custom model evaluation tool designed to adjust various networks and parameters, facilitating the analysis of the best algorithmic solutions for on-chain MEV extraction. Experimental results indicate that the RL-BES system excels in multiple DeFi applications. It demonstrates faster convergence and consistently surpasses the performance of Flashbot and other similar detection tools.

With the development of artificial intelligence and big data, data has become an important part of production factors, and the sharing and transaction of data have a very high importance. Through the storage service of the cloud data transaction platform, users can send data to the cloud platform remotely, and flexibly access and transmit data through the Internet anytime and anywhere. However, this approach faces growing data security concerns. When users transmit data to the cloud, they will not have full control over their data. Data stored in the cloud may be altered, deleted, leaked, or misappropriated, especially in public cloud environments. Many current data transaction platforms simply adopt a decentralized model to avoid this problem, but still perform poorly in the face of massive transaction scenarios. In addition, when the data demander receives the required data, there is the problem of denying the transaction, which challenges the availability of the data transaction platform and affects the trust of the data transaction participants in the transaction platform. This paper proposes a secure searchable-encryption-based data transaction protocol (SDTP) utilizing blockchain technology and searchable encryption. In the proposed protocol, the transaction platform does not gain access to the provider’s raw data, and the data provider has all decisionmaking rights over the data. The data demander can search for the target encrypted data using only keywords before receiving the original data authorized by the data provider. In addition, blockchain technology, with its decentralized and tamper-proof characteristics, has made important contributions to the transformation of traditional centralized data transaction platforms, and the entire data transaction process is recorded on the blockchain, effectively preventing problems such as demander denial and data tampering. In this paper, a formal verification tool is used to ensure that the proposed protocol meets the ideal security standard expected by the secure data transaction protocol, and the security of the protocol against attacks is proved from the perspective of non-formal theoretical analysis.