To enhance blockchain throughput, various parallel mechanisms have been proposed, yet many are ill-suited for complex assets due to the intricacy of their states and properties, resulting in longer dependency paths and reduced efficiency. This paper introduces a parallel mechanism tailored for complex assets by distinguishing between consensus and execute nodes and decoupling the transaction processing flow for independent scalability. It utilizes a multi-way Merkle tree to create independent state trees for assets and employs the MPT for a global state tree. Additionally, an efficient parallel state submission process is designed to improve state commit efficiency and support parallel transaction processing with an asynchronous workflow. The delayed submission strategy further eliminates blockages in the commit phase, enhancing system throughput. Experimental results show that this mechanism outperforms existing blockchain parallel mechanisms, significantly boosting state commit efficiency while ensuring high scalability.

Due to the distributed nature of federated learning (FL), the vulnerability of the global model and the need for coordination among many client devices pose significant challenges. As a promising decentralized, scalable and secure solution, blockchain-based FL methods have attracted widespread attention in recent years. However, traditional consensus mechanisms designed for Proof of Work (PoW) similar to blockchain incur substantial resource consumption and compromise the efficiency of FL, particularly when participating devices are wireless and resource-limited. To address asynchronous client participation and data heterogeneity in FL, while limiting the additional resource overhead introduced by blockchain, we propose the Directed Acyclic Graph-based Asynchronous Federated Learning (DAG-AFL) framework. We develop a tip selection algorithm that considers temporal freshness, node reachability and model accuracy, with a DAG-based trusted verification strategy. Extensive experiments on 3 benchmarking datasets against eight state- of-the-art approaches demonstrate thatDAG-AFL significantly improves training efficiency and model accuracy by 22.7% and 6.5% on average, respectively.

Federated Semi-Supervised Learning (FSSL) integrates Semi-Supervised Learning (SSL) with the federated framework, enabling clients to collaboratively train a global model using their local labeled and unlabeled data while preserving data privacy. Existing methods (e.g., FedMatch, FedSSL) rely on a central server to aggregate model parameters and utilize a centralized proxy dataset to guide the training process. However, these approaches face privacy risks, cause negative transfer due to data heterogeneity, and violate the lightweight design principle of federated learning. This paper proposes a decentralized framework, FedDSSL, which employs a dynamic topology structure to adaptively adjust the connection weights among clients, thereby enhancing collaborative efficiency. FedDSSL adopts a topology graph, where connection weights between clients are adaptively adjusted based on data similarity, enabling more efficient collaborative training. To replace the centralized proxy dataset, FedDSSL utilizes local self-supervised pre-training and crossclient knowledge distillation for regularization alignment. Additionally, FedDSSL introduces a distributed optimization strategy, employing multi-client collaborative validation and dynamic consistency regularization to improve the quality of pseudo-labels and model robustness. Experimental results demonstrate that FedDSSL outperforms mainstream methods in both IID and non-IID scenarios. It provides an efficient and lightweight solution for privacy-sensitive fields such as healthcare, significantly enhancing model robustness and generalization ability.

The current blockchain requires higher stability and efficiency. However, leaderBFT consensus and leaderlessBFT consensus have three problems: dominant replica failure, transaction interruption, and high communication overhead and latency. And regardless of leaderBFT consensus or leaderlessBFT consensus, there isn't a very good solution. It's urgent to leverage the advantages of both consensus types to enhance blockchain performance. To solve the problems, this paper proposes the ATBFT consensus protocol. ATBFT elects the dominant replica through reputation value to ensure higher reliability. In addition, ATBFT has an automatic switching mechanism between leaderBFT consensus and leaderlessBFT consensus. The protocol can automatically elect the consensus type according to the current network conditions, effectively utilizing the idle time of the view change to generate more blocks. Furthermore, it can guarantee the execution of the entire consensus process to ensure that the tx2-transactions are uninterrupted. Finally, ATBFT decomposes the blocks: the consensus is performed only through the block header, and the commitment and execution phase of a tx is performed through a linearly propagated block body. According to experimental evaluation, ATBFT has higher usability and practicality.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) can cooperate with other data platforms to improve recommendation performance while meeting the regulation and privacy constraints. However, FedRSs face many new challenges such as privacy, security, heterogeneity, and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this article, we: 1) summarize some common privacy mechanisms used in FedRSs and discuss the advantages and limitations of each mechanism; 2) review several novel attacks and defenses against security; 3) summarize some approaches to address heterogeneity and communication costs problems; 4) introduce some realistic applications and public benchmark datasets for FedRSs; and 5) present some prospective research directions in the future. This article can guide researchers and practitioners understand the research progress in these areas.

Academic qualifications and degrees are important manifestations of students' acceptance of higher education and important proof of social recognition. However, the existing certification system for academic qualifications and degrees has problems such as complex processes, centralization, and difficulty in tracing, leading to serious fraud and difficulty in meeting the comprehensive talent evaluation and data verification needs of employers. The article proposes privacy protection and data verification methods based on asymmetric encryption technology to protect the entire process of education authentication; At the same time, a smart contract upgrade method was proposed to support iterative development; And adopt an on chain and off chain collaborative storage solution to improve the operational efficiency of the blockchain system. Based on the existing problems in academic degree certification and combined with the above methods, a complete system for academic degree certification has been constructed. After testing, all functions of the system are good and meet the expected results.

Many existing cross-chain mechanisms do not adequately address all the characteristics of data assets and suffer from severe architectural limitations, resulting in inconsistency and security issues. While recent research has achieved cross-chain data asset transfer between distinct blockchains through a "freeze-commit" mechanism, their focus has mainly been on non-association, i.e., the status transmission of an asset is only related to itself. The "freeze-commit" mechanism introduces conflicts or potential double-spending across different chains and disrupts normal business processes for data assets with associations. To address this issue, we propose a dedicated mechanism for ensuring freeze-free consistency in cross-chain transfers of data assets. Instead of freezing the assets, we employ an incremental status updating approach to track status changes in the assets triggered by associated assets. The incremental status is then submitted and settled on the destination chain when the transfer process is completed. We provide analytical and experimental demonstrations to show that this mechanism effectively guarantees the consistency of data assets in cross-chain transfers while keeping the assets available throughout the process.

More and more business applications are crossing organization boundaries and typically involves a set of interactive organizations, known as cross-organization business process management. By taking as input the distributed event logs of each organization, cross-organization process mining techniques can reconstruct the underlying business process model to help process comprehension and improvements. Unfortunately, existing process mining techniques completely ignore the privacy issue, i.e., the privacy of the event log and business process model is not guaranteed. To cope with this challenge, this paper proposes a privacy-preserving cross-organizational business process mining framework based on blockchain and cryptography. Specifically, it mainly includes three steps: (1) each organization builds its private business process model, interaction messages, and collaborative tasks from its event log; (2) collaborative public process model for each organization is generated based on blockchain using privacy security intersection (PSI) cryptography algorithms to ensure the privacy of each organization; and (3) each organization combines its private business process model with relevant public process models, to obtain an organization-specific collaborative business process model. Using four public cross-organization datasets, the privacy-preserving ability and application of the proposed technique is demonstrated.

Crowdsourcing is a promising computing paradigm for processing computer-hard tasks by harnessing human intelligence. How to protect online workers' privacy is a hindrance for deploying crowdsourcing in the real world. Attempts have been made to address this issue by injecting noise or encrypting sensitive data, which cause quality loss and/or heavy computation and communication load. In this paper, we propose an approach, called FedTA (Federated Worthy Task Assignment for Crowd Workers), to protect a crowd worker's private data while ensuring quality. FedTA trains a client model based on the private data and annotations owned by a worker and uploads client models to aggregate the server model, without leaking the privacy of task data. To account for the varying task distributions (i.e., non-i.i.d.) and error-prone annotations of tasks, it leverages the feature similarity and semantic similarity separately derived from client and server models on local tasks, to quantify the quality of annotations and clients. Based on those, it further introduces a task assignment strategy to notify the clients which tasks are worthy and suitable for annotations. This strategy can incrementally improve the performance of client and server models. At the same time, it disregards the unworthy tasks to save the budget and to avoid their negative impact. Experimental results show that FedTA can complete secure crowdsourcing projects with high quality and low budget.

Blockchain mandates that every node store the whole chain’s history in order to address trust issues in the network. And the storage requirement becomes extremely high, severely affecting the chain’s scalability. To solve such a problem, many optimizations of storage have been proposed. In this paper, existing ways of blockchain storage scalability are described in two categories: off-chain and on-chain. The off-chain way is combined with various distributed and nondistributed storage systems. And on-chain is optimized by changing its block structure, storage rules, or technology. Blockchain technology with scalable storage has been applied in the medical industry. We assess and contrast the methods’ latency, security, and cost. And we point out the problems and challenges of the existing approaches and give an outlook on the future.

With the development of blockchain, the demand for the cross-chain transfer of the on-chain digital assets, such as Non-Fungible Tokens (NFTs), is increasing. The notary scheme is a kind of cross-chain mechanisms with low complexity to achieve the above demand. Although many NFT cross-chain methods and protocols have been invented, the method based on the notary scheme has not been widely studied at this stage. Therefore, we improve the traditional notary scheme and propose an NFT cross-chain transfer model with it. In this paper, we introduce NCTN, a trusted NFT cross-chain transfer model based on the notary group and design the cross-chain transaction protocol in detail according to NCTN’s architecture. To ensure the availability of the model, we then present an effective reputation value model, which is used as the basis for the reliable election in the notary group. Experiments show that our model can well complete the tasks of cross-chain transfers of NFTs and has good performance in transaction response time; our reputation value model can well avoid the problem of excessive centralization and can ensure the reliability of notaries.

Federated learning (FL), an emerging decentralized machine learning paradigm, supports the implementation of common modeling without compromising data privacy. In practical applications, FL participants heterogeneity poses a significant challenge for FL. Firstly, clients sometimes need to design custom models for various scenarios and tasks. Secondly, client drift leads to slow convergence of the global model.Recently, knowledge distillation has emerged to address this problem by using knowledge from heterogeneous clients to improve the model’s performance. However, this approach requires the construction of a proxy dataset. And FL is usually performed with the assistance of a center, which can easily lead to trust issues and communication bottlenecks. To address these issues, this paper proposes a knowledge distillation-based FL scheme called FedDCM. Specifically, in this work, each participant maintains two models, a private model and a public model. The two models are mutual distillations, so there is no need to build proxy datasets to train teacher models. The approach allows for model heterogeneity, and each participant can have a private model of any architecture. The direct and efficient exchange of information between participants through the public model is more conducive to improving the participants’ private models than a centralized server. Experimental results demonstrate the effectiveness of FedDCM, which offers better performance compared to s the most advanced methods.

Edge computing has been widely used to deploy and service deep learning applications. Equipped with GPUs, edge nodes can process concurrent incoming inference requests of the deep learning model. However, existing methods for inference tasks do not allow efficient parallel handling of user requests. This paper investigates the popular Transformer deep learning model and develops ParaTra, a parallel transformer inference framework for providing parallel inference services to users. In the framework, the Transformer model is partitioned and deployed in users’ devices and the edge node to efficiently utilize their processing power. The concurrent inference tasks with different sizes are dynamically packaged in a scheduling queue and sent in batch to an encoder-decoder pipeline for processing. ParaTra can significantly reduce the overheads of parallel processing and the usage of GPU memory. Experiment results show that ParaTra can save up to 37.1% of GPU memory usage and improve 8.4 times of processing speed.

Addressing untrustworthy behavior in cross departmental business process collaboration is the focus of current research. The untrustworthiness of the business process not only leads to the misuse and leakage of business data, but also leads to cheating by the participants in the business process. This paper proposes a blockchain-based trustworthy cross-departmental business process collaboration service mechanism(TBPCS) to solve the above problems. This paper firstly maps the participants, data and data ownership of the business process to the blockchain, which prevents the business process from being tampered with. This method allows the participants in the business process to act under the constraints of a trusted environment, avoiding the illegal use of business data. Then this paper maps the business process to the blockchain in the form of smart contracts, which ensures crossdepartment business process collaboration is trustworthy and reduces the verification cost of business data. This paper innovatively proposes a rollback mechanism that supports business interruptions. The mechanism ensures that the business process is trustworthy even in abnormal situations.

In the medical scenarios, the demand for secure sharing of medical data and trusted federated computing continues to increase, and blockchain technology can provide secure, credible, and tamper-resistant capabilities, which makes it necessary to combine the two. However, the full application of blockchain to medical scenarios faces two issues. Medical assets such as medical cohort data with strong data correlation and large scale are difficult to be accurately described and safely operated by blockchain. Transactions such as disease diagnosis and hospitalization prediction with many parameters are difficult to execute concurrently in blockchain. Therefore, we propose MAA model, which closely associates patients with assets, separates the logic of asset operations from its storage. At the same time, we propose OPE model with double-layer pipeline concurrency. By constructing the Dependency Graph, and generating multiple blocks with low conflict rates, which are executed simultaneously among multiple Execute Groups, OPE supports the high concurrent execution of medical transactions with high computing power requirements such as trusted federated learning. Experiments show that our model supports multiple types of medical data on-chain compared to existing models, and the concurrency is increased by at least 40% in a high-conflict medical environment.

In the existing public chain environment, most incentive mechanisms are for miners, who can obtain rewards after the packaged blocks are put on the blockchain. Compared with the public chain, the consortium chain network does not have the concept of coin rewards. The number of consensus nodes in the consortium chain network is limited, and the consortium chain forces nodes to provide services when they are admitted. Not only that, the transactions in the consortium chain are more complicated. Therefore, the original incentive mechanism based on miners and transaction gas is no longer applicable to the consortium chain environment. It has become a trend to consider the incentive model from the perspective of the participants. This paper proposes a participant-based incentive mechanism for the consortium chain, which is aimed at all participants. Based on the complexity of transactions, this paper proposes a multi- dimensional transaction ranking strategy to motivate participants. This strategy ensures that active participants have priority on- chain rights and inactive participants can also meet their needs, and solve the problems of malicious attack, transaction starvation and so on. Experiments show that our incentive mechanism can adapt to multiple scenarios and provide an active environment for consortium chain.

Artificial intelligence (AI) and blockchain are two of the most disruptive technologies in recent years. Blockchain is widely regarded as a trust machine because of its decentralization, non-tampering, anonymity and traceability. AI provides machines with cognitive functions, including learning, reasoning, and adaptation based on the collected data, which enables human-like machines possess intelligence and decision-making capabilities. Also, both technologies are data-driven, and thus there are rapidly growing interests in integrating them for trustworthy artificial intelligence and intelligent blockchain. In this paper, we review the related research on the integration of AI and blockchain, mainly analyzing how blockchain technology can improve AI from five aspects and pointing out the future research direction of these two technologies. And our research shows that blockchain can drive various components of AI including data, algorithms, and computational power to higher levels.

The digital representation of credentials has become a necessary way in all aspects of human life, such as healthcare, education, etc. However, the current digital credentials sharing solutions tend to overlook the problem of over-disclosure. The data presentation of credentials is an all-or-nothing process, which results in the leakage of unnecessary data and threatens the privacy of the holder. In this paper, to achieve authenticated selective disclosure of credentials, we first design a hybrid storage model incorporating erasure coding (EC), where the raw data are outsourced to an off-chain distributed storage service provider while only small digest information are stored on-chain to maintain data integrity. Moreover, under the storage model, we propose an authenticated data structure (ADS) which integrates EC and the Merkle B-tree to minimize data sharing. Based on this ADS, a verifiable object (VO) can be generated, which is used to provide proof of the disclosed data without exposing the other data of the credentials. At last, we prove the security of the proposed ADS scheme and the experimental results show that, compared to a baseline solution, the proposed ADS reduces the average building and verification time, without sacrificing much of the transmission cost.

When the medical services surrounding patients cannot meet the needs of patients, transfer treatment has become an unavoidable part in the current medical environment. Initially, people choose the transfer path within their own cognitive range. With the development of the Internet, people can obtain the transfer paths of patients similar to them from all over the country through the Internet for reference, further combine machine learning models such as RNN, LSTM, and CNN to recommend the best transfer path. However, the treatment behaviors usually span multiple medical institutions, and it is difficult to comprehensively and efficiently share the cases, examination results and treatment process between the institutions. In addition, the source of traditional prediction model’s data set is opaque, and the integrity of data needs to be verified, which all affect the accuracy of prediction result. In order to solve the above problems, we improve the traditional LSTM, and propose OO-LSTM which integrates data sets on the blockchain and off the blockchain, uses the analysis method of transfer path based on blockchain association and traceability mechanism, and supplements and cross-validates the data on the chain and off the chain. Based on OO-LSTM, we can obtain a reliable and complete prediction data set and provide more accurate and credible recommendations for patients according to the conditions of patients in the current medical institutions. Experiments have proved that our method has higher credibility and more accurate results than traditional prediction models at the same cost.

The rapid development of the Internet has brought new directions for improvement in e-commerce and promoted the pace of e-commerce to smart business. There are a lot of articles focusing on improving business processes efficiency, especially on business process nodes' optimization, using machine learning and even deep learning methods. Aiming at the problem of chaotic nodes' arrangement in traditional business process, this paper proposes a node optimization method, which can reconfigure business process nodes-integrating similar business nodes and decomposing complex business nodes. The algorithm proposed in this paper firstly vectorizes the process nodes to n-dimensional vectors, then uses the improved density clustering mining algorithm which is based on information entropy of clustering results to reassign the nodes. The information entropy can fairly evaluate the degree of system's confusion, so we can introduce information entropy to density clustering, then the clustering result will be evaluated and the algorithm will iteratively cluster until an optimal result is achieved. Finally, this paper conducted two experimental analysis on four business processes, successfully completed the optimal configuration of the nodes.

Non-Fungible Token (NFT) has garnered remarkable attention to decentralized digital asset management. Permissionless blockchains store NFT content data leveraging P2P storage networks, in which data resource flows subject to financial incentives. For permissioned blockchains, without incentives, the placement of content data to the storage network requires a sound strategy, including the placement process and the replica location strategy, to avoid problems such as communication cost excess and storage unfairness, which greatly limit the service efficiency and sustainability of the storage network. Therefore, in this paper, we propose a new collaboration model between blockchain and the P2P storage network for issuing a new NFT, in which blockchain can complete the placement process and promote rational data distribution in the P2P storage network. Our proposed replica location strategy mainly considers three factors: storage fairness, service efficiency, and business load. Through theoretical analysis and experiments, it is proved that our replica location strategy has a better performance in both fairness and efficiency.

Internet cooperative office environment promotes the development and application of workflow technology, achieving an official pattern of cross-regional and cross-sectoral. The cooperative office deals with the issue of spatial distance constraints in the interaction of business activities. However, the original staff cooperative mode in the existing office environment reflects the problems of cost of large and low efficiency and other issues. Fortunately, massive business workflow logs and database logs provide a possible way for understanding the running of the process. How to use the log data of the process and find the unnecessary cooperation in the original process model, as well as optimize the process model are the urgent problems to be solved at present. In this paper, we do the analysis of multilevel logs from database logs to workflow logs. Through the mining of the workflow log data and the exploring of the correspondence between the activities and roles in the business process, a structure identifying method is proposed. This method reconstitutes the correspondence between activities and roles based on the role permission redistribution and roles merging corresponding to the excessively decentralized subtasks. As a result, some unnecessary cooperative time can be reduced by removing the unnecessary roles and the running efficiency of the process can be improved to a greater extent.

Improving efficiency and performance is an important topic in the world today. As it is well-known, cooperative computing is an effective and traditional approach, and it is widely used in various fields. Inspired by this idea, take E-commerce for example, Security is one of its important indicators. In E-commerce, the security technology has become a major issue restricting the rapid development and popularization of E-commerce. Existing solutions leverage blockchain protocols to improve the credibility of transactions, but most of them have some limitations, such as a lower throughput and higher consensus latency, and these problems make blockchain technology difficult to be widely used. This paper presents a trusted framework (ETT F) using blockchain protocol in E-commerce to achieve a higher credible trading. ETTF includes a peer blockchain protocol (PBP) based on a peer blockchain architecture to support the storage of massive transactions and instant transactions. In PBP, the throughput scales are nearly linearly increased with the computation: the more computing power available, the more blocks are selected per unit time. Besides, in order to ensure a higher security of transactions we have introduced a strong consensus algorithm(ECA) in E-commerce. ETTF is also efficient because the number of messages it requires is nearly linear in the network size. Compared to Bitcoin-derived blockchain, ETTF shows better performance on throughput, latency, and capacity in E-commerce.

Process frameworks contain the main logic of business. Frameworks extracting and filling can formulate a new process efficiently and accurately. Aiming at the construction guidance of new business processes with framework, a direct-viewing solution based on the similarity analysis of process diagrams is proposed. In this solution, the process diagrams are abstracted into process trees to get process sequences and then the similarity is calculated out of process skeletons and process entities, thus the processes which are similar both in structure and function can be found. The process trees and process sequences refer to simple data structures that are easy to analyse and enhance process comprehensibility. In addition, similarity measurement from both the skeletons and the entities can control process analysis from the overall situation and avoid information omission and deviation. Meanwhile, general process frameworks are extracted out for reference by clustering the processes through improving similarity cluster algorithm, and the instructive recommendation to the processes under construction can be given according to the existing processes. Based on this solution, economic expenditure can be reduced to a large extent and existing resources can be reused.

The response time for multi-tenant queries is one of the most important indicators in the service level agreements (SLA). The service provider tries to optimize query scheduling strategy to finish queries of different tenants before the deadline to avoid penalty due to jeopardizing the SLA. With continuous expansion of tenants scale, peer-to-peer (P2P) structure becomes more and more popular in organizing and managing multi-tenant data. In this paper we propose an acentric scheduling approach for SLA-based multi-tenant queries according to the distribution characteristics of multi-tenant data. Our scheduling approach deploys multiple scheduling engines on the computing nodes in the cloud, where the computing node of each engine schedules its assigned queries, estimates whether these queries could be finished before the deadline, and migrates the queries that might jeopardize the SLA to another engine which can respond to it before the deadline. Since the high efficiency of the scheduling process is critical, we improve the balanced binary tree and use it to organize queries on each computing node. Using this structure, the online time complexity of the scheduling strategy is [Formula: see text]. Our extensive experiments demonstrate that our scheduling strategy is sufficient to meet the high scheduling efficiency requirement, while the penalty cost can be reduced up to [Formula: see text] compared with the benchmarking solution and with a better scalability.