On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

Blockchain-Based Decentralized Reputation Management System for Internet of Everything in 6G-Enabled Cybertwin Architecture

Internet of Everything(IoE)has emerged as a promising paradigm for the purpose of connecting and exchanging data among physical objects and humans over the Internet,and it can be widely applied in the fields of industry,transportation,commerce,and education.Recently,the emergence of 6G-enabled cybertwin network architecture provides the technical and theoretical foundation for the realization of IoE paradigm.However,the IoE has three open issues in the 6G-enabled cybertwin architecture,i.e.,data authenticity,data storage and node reliability.To address these issues,we propose a blockchain-based decentralized reputation management system(BC-DRMS)for IoE in 6G-enabled Cybertwin architecture.In the proposed BC-DRMS,the traffic data collected from end nodes is stored on the blockchain and the decentralized file system,i.e.,InterPlanetary File System(IPFS),to resist data tampering,and then the data is further processed by the edge clouds and core clouds to provide services to users.Also,a multi-level reputation evaluation scheme is designed to compute the reputation scores of IoE nodes to prevent malicious node attacks.The experiment results and analysis demonstrate that,compared to the traditional centralized reputation management systems(CRMS),the proposed BC-DRMS cannot only address the issues of data authenticity and storage,but also provides high reliability for IoE in 6G-enabled cybertwin architecture.

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation management.Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced data.However,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update module.This paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system.Here,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance.Computation of user reputation scores relies on their behavioral history,actively discouraging undesirable conduct.Additionally,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and reputation.Our scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior.This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

As 5G becomes commercial,researchers have turned attention toward the Sixth-Generation(6G)network with the vision of connecting intelligence in a green energy-efficient manner.Federated learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication efficiency.However,designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients'contributions during the learning process.In this paper,we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated learning.The proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated learning.Meanwhile,clients’contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks,which satisfies availability,fairness,and additivity.The simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

How Do Young Children Manage Their Reputation among Peers?

Young children’s reputation management is closely related to their social development.The purpose of our study is to examine the interaction between theory of mind and partner choice on children’s reputation management.Participants consisted of 270 children who were 3 to 5 years old.First,we measured participants’theory of mind capabilities using the unexpected location task and unexpected content task and then randomly divided the participants into the control group,non-partner–choice group,and partner-choice group.We measured reputation management by comparing children’s willingness to share and sharing behavior between these groups.Thefindings are as follows:(1)Children from ages 3 to 5 demonstrated reputation management,and their reputation management followed a significant developmental trend.The reputation management of 4-to 5-year-old children was significantly better than that of 3-year-old children.(2)Scores on the theory of mind tasks positively predicted children’s reputation management.(3)Partner choice affected children’s reputation management.In the partner-choice group,children’s reputation management was more apparent.(4)Partner choice did moderate the relationship between theory of mind and children’s reputation management.In the partner-choice group,theory of mind had a stronger predictive effect on children’s reputation management.

Research on Intelligent Mobile Commerce Transaction Security Mechanisms Based on Mobile Agent

In networked mobile commerce network transactions,trust is the prerequisite and key to a smooth transaction.The measurement of trust between entities involves factors such as transaction amount,transaction time,personal income of consumer entities and their risk attitude towards trust,etc.,so it is difficult to accurately calculate quantitatively.In order to find out the essential characteristics of this trust relationship,based on the research background of mobile commerce in the mobile network environment,a dynamic trust mechanism is proposed through the research of trust in the mobile network environment,trust influencing factors and trust mechanism.The calculation model of mobile interactive services based on mobile service business transactions.The model calculates feedback credibility through feedback deviation and feedback robustness,and combines transaction context factors and trust mapping mechanism to judge the seller’s credibility.This model better reflects the degree of influence of subjective factors such as personal preferences and risk attitudes on trust calculations,And the sensitivity of trust algorithms and transaction attributes has been greatly improved.After a large number of experiments and theoretical analysis,this mechanism provides an effective explanation for solving the problem of network trust computing.and provides valuable new ideas for the study of secure transactions in the mobile Internet environment.

A Threshold Scheme under Peer-to-Peer Network

Resource sharing among peers is one of important application in Peer-to-Peer（P2P） network. Inexistenee of server makes P2P network to be a less trustable tool for property owners to distribute their resource. In order to protect intelligent property, reputation management strategy is adopted in many P2P network. As long as a P2P network reputation strategy is confirmed, application designer can employ a special detailed distribution scheme to fulfill content distribution within the net. Shmir （k, n） threshold scheme, for example, is an encryption scheme to enhance the distribution se curity of this kind of design. （k＊, n） threshold scheme is a new tool similar to Shmir scheme is proposed in this paper. The new scheme based on polynomial expansion and its security is decided by the one way function used in the secret distribution procedure. The scheme efficiency and its features as well as comparison between new and Shmir scheme are also discussed in this paper.