The immutability is a crucial property for blockchain applications, however, it also leads to problems such as the inability to revise illegal data on the blockchain and delete private data. Although redactable blockchains enable on-chain modification, they suffer from inefficiency and excessive centralization, the majority of redactable blockchain schemes ignore the difficult problems of traceability and consistency check. In this paper, we present a Dynamically Redactable Blockchain based on decentralized Chameleon hash (DRBC). Specifically, we propose an Identity-Based Decentralized Chameleon Hash (IDCH) and a Version-Based Transaction structure (VT) to realize the traceability of transaction modifications in a decentralized environment. Then, we propose an efficient block consistency check protocol based on the Bloom filter tree, which can realize the consistency check of transactions with extremely low time and space cost. Security analysis and experiment results demonstrate the reliability of DRBC and its significant advantages in a decentralized environment.

To guarantee the liveness of Byzantine fault-tolerant state machine replication (BFT-SMR) protocols in asynchronous environments, asynchronous fallback strategies are frequently utilized to replace traditional leader-based view change mechanisms. However, leader-centric protocols have certain limitations: (1) a single leader may create a system bottleneck, making it difficult to guarantee liveness when under attack. (2) transitioning between partially synchronous and asynchronous protocols presents a challenge in achieving a balance between throughput and latency. To solve these problems, this paper proposes DyBFT, a leaderless BFT-based protocol. Each replica replaces the traditional leader election mechanism and dual-path switching mechanism by a scheme that locally adjusts the local valid committee set. The paper presents the implementation of two variations of DyBFT, namely DyBFT1 and DyBFT2. DyBFT1 utilizes parallel path proposals and local block commits without requiring additional communication overhead, effectively mitigating performance degradation resulting from an increase in the number of replicas and transaction volume. DyBFT2 optimizes commit rules to achieve higher throughput, low latency and stability. Experimental results demonstrate that, compared to existing solutions, DyBFT achieves lower latency and higher throughput in large-scale and unpredictable network environments.

In IoT with Blockchain, an increasing number of mobile IoT devices are starting to work across various locations, requiring operators connected to these IoT devices to provide roaming services. Due to the differences in the blockchains used by operators, a safe and efficient cross-chain model for IoT device roaming settlement is significant for operators. In this paper, a cross-chain model based on credit hierarchical multi-notary is proposed to connect different blockchains in IoT devices roaming settlement. Firstly, a cross-chain election method by sub-notary group is proposed to solve the problem of “Richer Get Richer” in blockchain with credit scheme and avoid risk of malicious notary nodes. Secondly, a re-election mechanism is used to make the process of cross-chain running properly with the number of IoT devices is increasing rapidly and transaction concurrency. Moreover, a weighted voting system is designed for sub-notary group cross-chain model to reduce the influence of malicious notary nodes. Experimental results demonstrate that the proposed cross-chain model performs better on credit ecology and defending malicious nodes’ influence than the model without sub-notary mechanism.

With the rapid proliferation of integration of blockchain and IoT, traditional centralized architectures and single-node processing have been effectively solved, but scalability of blockchain have become inadequate to meet the demands of high-load environments. Sharded blockchain brings an innovative approach to tackle the scalability of blockchain and has the potential to greatly enhance the performance of IoT based on blockchain. This paper introduces CTS towards IoT – complete tree sharding that overlaps shards to enable a little shards to retain records of other shards, facilitating the validation of cross-shard transactions as intra-shard transactions. A tree sharding consensus mechanism that relies on the complete tree architecture is proposed to ensure consistency among interconnected shards during the execution of cross-shard transactions. A prototype for CTS towards IoT is simulated and the findings demonstrate that CTS can enhance transaction throughput by a factor of 1.3 when compared to state of the art layered sharding systems observed in a system consisting of 15 shards and 1500 nodes.

Current blockchain payment approaches face practical application challenges due to high decryption complexity, extended ciphertext lengths, and reliance on semi-honest operational models. To address these issues, this paper presents a batch payment scheme with denomination privacy using the Castagnos-Laguillaumie (CL) homomorphic encryption and batch zeroknowledge proofs. Our scheme reduces decryption complexity and ciphertext length while enabling malicious model operations. It supports efficient batch payments to multiple recipients and includes features for payment statistics. Experimental results show an average encryption and decryption time of 0.89 ms and 1.55 ms, respectively, confirming its practicality and speed.

The metaverse is a virtual environment that combines the real and digital worlds through technological and social structures. It heralds a novel paradigm in internet applications and societal engagement by weaving together diverse cutting-edge technologies to facilitate a virtual representation of the tangible world. Within the metaverse, the facets of economy, culture, and other dimensions are fully documentable, trackable, and quantifiable, largely owing to the capabilities of blockchain technology. Nonetheless, the transactional volume within the metaverse is typically substantial, posing challenges for a singular blockchain platform in terms of efficiency and support capacity. Consequently, a multi-blockchain infrastructure becomes essential to underpin the economic fabric of the metaverse. Enabling the collaborative operation of multiple blockchain platforms, specifically achieving the seamless transfer of value across different blockchain ecosystems, has emerged as a critical challenge. To address this challenge, this paper introduces a cross-chain protocol predicated on a multi-role notary system designed to facilitate inter-blockchain value transfers. The protocol specifies the functions of different entities within the notary framework, allocating specific duties to notaries, committees, and a leader. It also promotes mutual oversight among notaries to sustain a dynamic and equitable group structure. Furthermore, an incentive mechanism is proposed to motivate committee members toward prompt and judicious decision-making regarding votes. Based on experiments conducted on the Ethereum platform, our proposed solution exhibits a 75% reduction in transaction time compared to Ethereum's transaction confirmation time. This paper introduces a cross-chain protocol based on a multi-role notary system, aiming to facilitate value transfer between blockchains and construct the economic framework of the metaverse using a multi-chain system. It also provides a security analysis and performance analysis of the scheme to illustrate its advantages. image

In the current landscape of federated learning, ensuring the verifiability of aggregated results and guaranteeing the security and authenticity of gradients are essential requirements. Additionally, distributed pseudonymous tracking of participants is required. In this paper, we propose a novel secure data aggregation scheme that simultaneously fulfills all of these requirements. Our scheme incorporates blind signatures and random matrix encoding techniques to ensure the accomplishment of distributed pseudonym tracking and the verifiability of secure gradient aggregation results. Building upon this foundation, the scheme incorporates a commitment system to verify the authenticity of aggregated data returned by the server, thus preventing collusion attacks between participants and the aggregation server. A thorough analysis of accuracy and security confirms that the proposed solution meets the intended design requirements.

Current data trading systems only support plaintext or unaudited private transactions. To overcome these, we present a confidential batch payment scheme with integrated auditing for enhanced data trading security. We use Castagnos–Laguillaumie (CL) homomorphic encryption and batch zero-knowledge proofs to construct the scheme. The scheme reduces decryption complexity and ciphertext length while enabling malicious model operations. In addition, it supports efficient batch payments to multiple recipients and includes features for payment statistic analysis and auditing. Experimental results indicate that the system efficiently handles encryption, decryption, and auditing tasks, completing each operation in an average of 0.89, 1.55, and 1.55 milliseconds, respectively.

Forest fire detection and resource monitoring are the focus of forest resource protection, but they often suffer from internal attacks. Insider attackers try to get away with illegal activities such as illegal logging and encroachment on forest resources by manipulating data. Illegal activities pose a huge threat to forest resources, so defending against internal attacks is key to forest regulation systems. This paper presents a blockchain-based forest fire detection system (FFD) in a multi-party environment. FFD introduced the blockchain + database architecture for data storage and designed a hybrid encryption and decryption algorithm using CP-ABE and AES algorithms to realize data sharing and access control so as to ensure data security. The artificial intelligence technology is employed to identify fires, reducing the human factor in fires. Theoretical analysis and experimental data show that FFD has high performance and is suitable for forest fire detection and monitoring.

Due to the rapid development of demand response management and distributed energy resources, prosumers are becoming more proactive, which also promotes the emergence of peer-to-peer (P2P) energy trading mechanisms. However, we find that it is quite difficult to simultaneously achieve high computational efficiency, decentralized operations and solution optimality in a P2P energy trading mechanism, which is called the “P2P energy trading trilemma”. In this paper, we first propose a novel negotiation mechanism for P2P energy trading that can maximize social welfare in a decentralized manner and respect physical network constraints. Then, the local optimization problem is transformed into a closed-form alternating update algorithm (AUA), so that the computational efficiency can be greatly improved. Therefore, our proposed mechanism solves the “P2P energy trading trilemma" to a certain extent. Another challenge is that the locational marginal price (LMP)-based market mechanism cannot satisfy the incentive-compatible property. To encourage prosumers to cooperate in a lack-of-trust environment, a novel incentive-compatible mechanism is proposed for P2P energy trading using consensus mechanism inspired by proof of solution (PoSo) and smart contract (SC). Finally, we simulate the functionality of the mechanism in terms of convergence performance, reliability, scalability, computational efficiency, and SC operations.

As a distributed machine learning paradigm, federated learning has attracted wide attention from academia and industry by enabling multiple users to jointly train models without sharing local data. However, federated learning still faces various security and privacy issues. First, even if users only upload gradients, their privacy information may still be leaked. Second, when the aggregation server intentionally returns fabricated results, the model’s performance may be degraded. To address the above issues, we propose a verifiable privacy-preserving federated learning scheme VPPFL against semi-malicious cloud server. We use threshold multi-key homomorphic encryption to protect local gradients, and construct a one-way function to enable the users to independently verify the aggregation results. Furthermore, our scheme supports a small portion of users dropout during the training process. Finally, we conduct simulation experiments on the MNIST dataset, demonstrating that VPPFL can correctly and effectively complete training and achieve privacy protection.

Artificial intelligence has immense potential for applications in smart healthcare. Nowadays, a large amount of medical data collected by wearable or implantable devices has been accumulated in Body Area Networks. Unlocking the value of this data can better explore the applications of artificial intelligence in the smart healthcare field. To utilize these dispersed data, this paper proposes an innovative Federated Learning scheme, focusing on the challenges of explainability and security in smart healthcare. In the proposed scheme, the federated modeling process and explainability analysis are independent of each other. By introducing post-hoc explanation techniques to analyze the global model, the scheme avoids the performance degradation caused by pursuing explainability while understanding the mechanism of the model. In terms of security, firstly, a fair and efficient client private gradient evaluation method is introduced for explainable evaluation of gradient contributions, quantifying client contributions in federated learning and filtering the impact of low-quality data. Secondly, to address the privacy issues of medical health data collected by wireless Body Area Networks, a multi-server model is proposed to solve the secure aggregation problem in federated learning. Furthermore, by employing homomorphic secret sharing and homomorphic hashing techniques, a non-interactive, verifiable secure aggregation protocol is proposed, ensuring that client data privacy is protected and the correctness of the aggregation results is maintained even in the presence of up to t colluding malicious servers. Experimental results demonstrate that the proposed scheme’s explainability is consistent with that of centralized training scenarios and shows competitive performance in terms of security and efficiency.

Blockchain technology is a strategic technology to support the development of digital economy, which helps to promote data sharing, improve the efficiency of communication and build a trusted system. With the continuous development of blockchain technology, security problems caused by lack of regulation are also frequent. Also, the supervision process needs to check the data information on the blockchain, which may lead to the disclosure of users’ privacy information. To solve the above problems, this paper proposes a privacy protection blockchain supervision scheme (PBS) that supports ciphertext supervision and malicious user tracking. The PBS supports the supervision of ciphertext data on the blockchain, meaning that regulators do not need to decrypt it and can perform supervision without plaintext. This ensures that the private data of users on the blockchain would not be compromised in the process of supervision. Moreover, the scheme supports the tracking of the sender of the offending information. Theoretical analysis and comparison show that the proposed PBS can effectively ensure the privacy of user data on blockchain, and experimental analysis demonstrates the practicability of the scheme.

The development of the Internet of Medical Things has made online diagnostic systems an attractive application. However, the opacity and insufficient supervision of cloud computing can result in problems such as data leakage and unreliable outcomes in diagnostic systems. In order to solve these problems, this paper proposes a verifiable privacy-preserving online diagnosis scheme (VPOD) based on multiclass SVM. This solution uses CKKS leveled homomorphic encryption to build a secure inner product computation protocol and a secure decision function computation protocol to achieve privacy protection for multiclass SVM model parameters, patient medical data and diagnosis results. In addition, this article designs a verification mechanism for multiclass SVM model predictions to ensure the correctness of the results. Performance evaluation shows that VPOD is more functional, has low computational overhead for patients and hospitals, and has an accuracy of 98.64% on real data sets.

The paper introduces a novel approach to address secure aggregation in federated learning, utilizing a multi-server model. This demonstrably secure system effectively mitigates the issues of server loss and single point of failure. The protocol established for secure aggregation operates under the common reference string model. It employs an additive homomorphic secret-sharing scheme alongside a homomorphic Chameleon hash function. This combination results in substantial enhancements in performance, particularly in reducing communication and computational expenses. These improvements have been empirically validated through rigorous testing, showcasing the protocol’s efficacy compared to existing alternatives.

Sharding is one of the key technologies of blockchain scalable characteristics widely used in cryptocurrencies, Internet of Things, supply chain management and other fields. It can achieve high performance scaling without reducing the decentralization of blockchain, thus solving the problems of insufficient scalability and low throughput of blockchain. In this paper, the characteristics of some classical sharding technologies are analyzed in terms of performance and implementation. The key mechanisms of sharding are summarized, including sharding formation, reshuffle, intra-shard consensus protocol, and cross-shard protocol. Also, the advantages and shortcomings of typical scalability schemes are summarized, including state channel, DAG, and side chain. We summarize the current challenges faced by sharding technology from intra-shard, and cross-shard, and provide an outlook on the development prospects and future research directions in this field.

The fast expansion of the Internet, as well as people’s concern for personal privacy and security, have raised the expectations for the identity authentication process. Although current controlled anonymous authentication techniques may provide anonymous authentication and supervision, they are inefficient. The one issue is the high processing cost of presenting and verifying the certificate. Another issue is that a single certification authority (CA) cannot reply timely when there are various requests for certificates and tracing fraudulent users. This article presents an efficient blockchain-based anonymous authentication and supervision system (EAAS) to overcome these issues. In comparison to previous solutions, our EAAS system adopts a double-layer CA architecture to address the issue that a single CA cannot react to a large number of requests in a short period of time. Additionally, it reduces the computational cost, making certificate presentation and verification more effective. Security analysis indicates that the proposed scheme enjoys anonymity, traceability, and unlinkability, and can resist forgery attacks. The theoretical and experimental comparison demonstrates its practicality in terms of presenting and verifying the certificate.

Smart grid is the future direction of the traditional power system, with characteristics such as high efficiency and stability. Intelligent pricing, as part of the functionality of smart grids, relies on collecting a large amount of user power data to analyze and adjust electricity prices, which may lead to the possibility of user privacy data leakage. To address these issues, we propose a privacy-preserving scheme for smart grids that supports intelligent pricing (PSR). A data aggregation mechanism is designed based on Paillier encryption and Schnorr signature technology, which can protect the privacy of user data and realize efficient aggregation of user power data in the control center. In addition, a proxy re-encryption technology is developed to enable secure sharing of user power data between users and service providers. Based on security analysis and experimental evaluations, the PSR scheme has stronger security and higher efficiency compared to existing schemes.

推进中国档案文献遗产工程建设与发展对于赓续中华文明、传承民族记忆、深化社会认同等具有重要意义.本文通过文献调研、网络调研、深度访谈等方法,发现中国档案文献遗产工程建设现存问题包括对文献遗产重视不够、相关法规政策较缺乏、资源后续开发动力不足、地区发展不平衡性凸显、人才和资金保障不足.本文提出有针对性的完善策略,如拓宽视野思维,培育文献遗产保护的社会氛围;完善法规体系,健全文献遗产"名录制度";优化入选标准,公开文献遗产评审流程;开展资源普查,建立文献遗产工程建设区域中心;创新开发形式,提升文献遗产社会影响力;整合社会力量,保障文献遗产工程资源投入.

"档案"一词已成为所有可想象的存储和记忆形式的普遍隐喻.然而,从媒介考古学的视角出发,德国学者沃尔夫冈·恩斯特认为,档案并非专门用于记忆,而是用于数据存储的纯技术实践:我们添加到档案中的任何故事都来自外部.档案没有叙事性的记忆,只有计数形式的记忆.恩斯特认为,在数字文化中,档案实现由档案空间到档案时间的演化,关键在于数据持续传输过程中的动态性.由此,档案成为真正意义上的"隐喻",蕴含无限的可能.