SemTrust: A Semantic Reputation System in P2P-Based Semantic Web

A reputation mechanism is introduced in P2P- based Semantic Web to solve the problem of lacking trust. It enables Semantic Web to utilize reputation information based on semantic similarity of peers in the network. This approach is evaluated in a simulation of a content sharing system and the experiments show that the system with reputation mechanism outperforms the system without it.

A Formal Reputation System for Trusting Wireless Sensor Network

In this paper, a formal system is proposed based on beta reputation for the development of trustworthy wireless sensor networks （FRS-TWSN）. Following this approach, key concepts related to reputation are formal described step by step for wireless sensor networks where sensor nodes maintain reputation for other sensors and use it to evaluate their trustworthiness. By proving some properties of beta reputation system, the beta distribution is founded to fit well to describe reputation system. Also, a case system is developed within this framework for reputation representation, updates and integration. Simulation results show this scheme not only can keep stable reputation but also can prevent the system from some attacks as bad mouthing and reputation cheating.

Application of Machine Learning for Online Reputation Systems

Users on the Internet usually require venues to provide better purchasing recommendations.This can be provided by a reputation system that processes ratings to provide recommendations.The rating aggregation process is a main part of reputation systems to produce global opinions about the product quality.Naive methods that are frequently used do not consider consumer profiles in their calculations and cannot discover unfair ratings and trends emerging in new ratings.Other sophisticated rating aggregation methods that use a weighted average technique focus on one or a few aspects of consumers′profile data.This paper proposes a new reputation system using machine learning to predict reliability of consumers from their profile.In particular,we construct a new consumer profile dataset by extracting a set of factors that have a great impact on consumer reliability,which serve as an input to machine learning algorithms.The predicted weight is then integrated with a weighted average method to compute product reputation score.The proposed model has been evaluated over three Movie Lens benchmarking datasets,using 10-folds cross validation.Furthermore,the performance of the proposed model has been compared to previous published rating aggregation models.The obtained results were promising which suggest that the proposed approach could be a potential solution for reputation systems.The results of the comparison demonstrated the accuracy of our models.Finally,the proposed approach can be integrated with online recommendation systems to provide better purchasing recommendations and facilitate user experience on online shopping markets.

EARep:A Fully Distributed Exchange-Based Anonymity P2P Reputation System

A fully distributed exchange-based anonymity P2P reputation system--EARep was proposed. EARep can provide all the peer＇s anonymity in a fully distributed structured manner. In EARep, each peer＇s anonymity is achieved by changing pseudonyms. Analysis and simulation results showed that compared with DARep, which is an existing representative scheme, our system has two main features. First, EARep increased anonymity degree （measured by linkage probability） and was much more scalable than DARep. Specifically, with the same message overhead, EARep reduced the linkage probability by more than 50% compared with DARep. Alternatively, to achieve the same anonymity degree, the message overhead in EARep was less than 25% of that in DARep. Second, the service selection success , tio of EARep was greater than 90% even when the percentage of malicious peers was up to 70%, which makes our system robust to malicious behaviors of peers.

Low Cost and Reliable Anonymity Protocols in P2P Reputation Systems

In peer-to-peer (P2P) reputation systems,each peer's trustworthiness is evaluated based on its pseudonym's rating values given by other peers. Since it is assumed that each peer has a long lived pseudonym,all the transactions conducted by the same peer may be linked by its pseudonym. Therefore,one of the fundamental challenges in P2P reputation systems is to protect peers' identity privacy. In this paper,we present two independent anonymity protocols to achieve all the peers' anonymity by changing pseudonym with the help of a trusted third party (TTP) server. Compared with RuP (Reputation using Pseudonym),an existing representative scheme,our protocols reduce the server's cost in two different ways. First,we propose a protocol using blind signature scheme as in RuP. The protocol improves the blind signature scheme and assessment of macro-node values,and reduces the server's cost by half in terms of encryption and decryption operations and message overhead. Second,we propose another protocol,group-confusion protocol,to further reduce the server's cost.

A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applications.This paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering packets.The proposed version employs a network-based reputation system to select the best and most secure path to a destination.To achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system.To minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation tables.Moreover,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast approaches.The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other protocols.Using the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing path.The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones.Furthermore,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

A Privacy-Preserving Attribute-Based Reputation System in Online Social Networks

Online social networks （OSNs） have revolutionarily changed the way people connect with each other. One of the main factors that help achieve this success is reputation systems that enable OSN users to mutually establish trust relationships based on their past experience. Current approaches for the reputation management cannot achieve the fine granularity and verifiability for each individual user, in the sense that the reputation values on such OSNs are coarse and lack of credibility. In this paper, we propose a fine granularity attribute-based reputation system which enables users to rate each other＇s attributes instead of identities. Our scheme first verifies each OSN user＇s attributes, and further allows OSN users to vote on the posted attribute-associated messages to derive the reputation value. The attribute verification process provides the authenticity of the reputation value without revealing the actual value to entities who do not have the vote privilege. To predict a stranger＇s behavior, we propose a reputation retrieval protocol for querying the reputation value on a specific attribute. To the best of our knowledge, we are the first to define a fine-grained reputation value based on users＇ verified attributes in OSNs with privacy preservation. We provide the security analysis along with the simulation results to verify the privacy preservation and feasibility. The implementation of the proposed scheme on current OSNs is also discussed.

Blockchain-Empowered Token-Based Access Control System with User Reputation Evaluation

Currently,data security and privacy protection are becoming more and more important.Access control is a method of authorization for users through predefined policies.Token-based access control(TBAC)enhances the manageability of authorization through the token.However,traditional access control policies lack the ability to dynamically adjust based on user access behavior.Incorporating user reputation evaluation into access control can provide valuable feedback to enhance system security and flexibility.As a result,this paper proposes a blockchain-empowered TBAC system and introduces a user reputation evaluation module to provide feedback on access control.The TBAC system divides the access control process into three stages:policy upload,token request,and resource request.The user reputation evaluation module evaluates the user’s token reputation and resource reputation for the token request and resource request stages of the TBAC system.The proposed system is implemented using the Hyperledger Fabric blockchain.The TBAC system is evaluated to prove that it has high processing performance.The user reputation evaluation model is proved to be more conservative and sensitive by comparative study with other methods.In addition,the security analysis shows that the TBAC system has a certain anti-attack ability and can maintain stable operation under the Distributed Denial of Service(DDoS)attack environment.

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.

Reputation evaluation method for open multi-agent systems

A reputation evaluation method based on multi-dimensional information representation and correlative algorithm is proposed for open multi-agent systems. First, a vector model is estab- lished to represent the reputation related information. Second, a vector based reputation model ＂TRUST＂ is put forward to evaluate the reputation of agents. Finally, a correlative algorithm for se- lecting the most appropriate service provider is proposed. Simulation results indicate that the method can quickly and accurately to achieve the aim of adaptive immunity to reputation fraud and improving the average gain that service consumer agents obtained.

On the Modeling of Honest Players in Reputation Systems

Reputation mechanisms are a key technique to trust assessment in large-scale decentralized systems. The effectiveness of reputation-based trust management fundamentally relies on the assumption that an entity＇s future behavior may be predicted based on its past behavior. Though many reputation-based trust schemes have been proposed, they can often be easily manipulated and exploited, since an attacker may adapt its behavior, and make the above assumption invalid. In other words, existing trust schemes are in general only effective when applied to honest players who usually act with certain consistency instead of adversaries who can behave arbitrarily. In this paper, we investigate the modeling of honest entities in decentralized systems. We build a statistical model for the transaction histories of honest players. This statistical model serves as a profiling tool to identify suspicious entities. It is combined with existing trust schemes to ensure that they are applied to entities whose transaction records are consistent with the statistical model. This approach limits the manipulation capability of adversaries, and thus can significantly improve the quality of reputation-based trust assessment.

A Load Service System with Reputation Proof in P2P Networks

In this paper, a new system design for load services in computer networks with a new reputation system is constructed. The use of the reputation system is to address the free-rider problem. Database systems are used by directory agents to save information provided by load-server agents. Protocols are built for how a host finds available servers for load service or load transfer, especially when a host moves to a new region. Detailed procedures include how a directory agent builds its database, how a load-server agent provides services, and how a load-client agent receives its desired services. The system uses the fuzzy logic control method to transfer loads for load balancing, instead of the method of a fixed threshold level. For an ad-hoc (wireless) computer network framework, this new system structure is aimed to provide efficient ways for hosts to communicate with one another and to access resources in the system. This will also help the users of networks locate resources in a most effective and secure manner.

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-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.

The Reputation Model of Multi-Stage Dynamic Game

Aim To study the implicit restriction mechanism for hidden action in multi stage dynamic game. Methods A reputation model for restriction on repeated principal agent relationship was established by using the theory on principal agent problem in information economics and the method of game theory to study the implicit restriction mechanism for hidden action. Results and Conclusion It is proved that there exists implicit restriction mechanism for the multi stage principal agent relationship, some conditions for effective restriction are derived, the design methods of implicit restriction mechanism are presented.

SLCRM： Subjective Logic-Based Cross-Layer Reputation Mechanism for Wireless Mesh Networks

Wireless Mesh Networks （WMNs） are vulnerable to various security threats because of their special infrastructure and communication mode, wherein insider attacks are the most challenging issue. To address this problem and protect innocent users from malicious attacks, it is important to encourage cooperation and deter malicious behaviors. Reputation systems constitute a major category of techniques used for managing trust in distributed networks, and they are effective in characterizing and quantifying a node＇s behavior for WMNs. However, conventional layered reputation mechanisms ignore several key factors of reputation in other layers; therefore, they cannot provide optimal performance and accurate malicious node identification and isolation for WMNs. In this paper, we propose a novel dynamic reputation mechanism, SLCRM, which couples reputation systems with a cross-layer design and node-security-rating classification techniques to dynamically detect and restrict insider attacks. Simulation results show that in terms of network throughput, packet delivery ratio, malicious nodes＇ identification, and success rates, SI_CRM imple- ments security protection against insider attacks in a more dynamic, effective, and efficient manner than the subjective logic and uncertainty-based reputation model and the familiarity-based reputation model.

Reputation-Based Hierarchically Cooperative Spectrum Sensing Scheme in Cognitive Radio Networks

Cooperative spectrum sensing in cog- nitive radio is investigated to improve the det- ection performance of Primary User （PU）. Meanwhile, cluster-based hierarchical coop- eration is introduced for reducing the overhead as well as maintaining a certain level of sens- ing performance. However, in existing hierar- chically cooperative spectrum sensing algo- rithms, the robustness problem of the system is seldom considered. In this paper, we pro- pose a reputation-based hierarchically coop- erative spectrum sensing scheme in Cognitive Radio Networks （CRNs）. Before spectrum sensing, clusters are grouped based on the location correlation coefficients of Secondary Users （SUs）. In the proposed scheme, there are two levels of cooperation, the first one is performed within a cluster and the second one is carried out among clusters. With the reputa- tion mechanism and modified MAJORITY rule in the second level cooperation, the pro- posed scheme can not only relieve the influ- ence of the shadowing, but also eliminate the impact of the PU emulation attack on a rela- tively large scale. Simulation results show that, in the scenarios with deep-shadowing or mul- tiple attacked SUs, our proposed scheme ach- ieves a better tradeoff between the system robustness and the energy saving compared with those conventionally cooperative sensing schemes.

A Dynamic Reputation–based Consensus Mechanism for Blockchain

In recent years,Blockchain is gaining prominence as a hot topic in academic research.However,the consensus mechanism of blockchain has been criticized in terms of energy consumption and performance.Although Proof-of-Authority(PoA)consensus mechanism,as a lightweight consensus mechanism,is more efficient than traditional Proof-of-Work(PoW)and Proof-of-Stake(PoS),it suffers from the problem of centralization.To this end,on account of analyzing the shortcomings of existing consensus mechanisms,this paper proposes a dynamic reputation-based consensus mechanism for blockchain.This scheme allows nodes with reputation value higher than a threshold apply to become a monitoring node,which can monitor the behavior of validators in case that validators with excessive power cause harm to the blockchain network.At the same time,the reputation evaluation algorithm is also introduced to select nodes with high reputation to become validators in the network,thus increasing the cost of malicious behavior.In each consensus cycle,validators and monitoring nodes are dynamically updated according to the reputation value.Through security analysis,it is demonstrated that the scheme can resist the attacks of malicious nodes in the blockchain network.By simulation experiments and analysis of the scheme,the result verifies that the mechanism can effectively improve the fault tolerance of the consensus mechanism,reduce the time of consensus to guarantee the security of the system.

Reputation-Based Cooperative Spectrum Sensing Algorithm for Mobile Cognitive Radio Networks

Identifying malicious users accurately in cognitive radio networks(CRNs) is the guarantee for excellent detection performance. However, existing algorithms fail to take the mobility of secondary users into consideration. If applied directly in mobile CRNs, those conventional algorithms would overly punish reliable users at extremely bad or good locations, leading to an obvious decrease in detection performance. To overcome this problem, we divide the whole area of interest into several cells to consider the location diversity of the network. Each user's reputation score is updated after each sensing slot and is used for identifying whether it is malicious or not. If so, it would be removed away. And then our algorithm assigns users in cells with better channel conditions, i.e. larger signal-to-noise ratios(SNRs), with larger weighting coefficients, without requiring the prior information of SNR. Detailed analysis about the validity of our algorithm is presented. The simulation results show that in a CRN with 60 mobile secondary users, among which, 18 are malicious, our solution has an improvement of detection probability by 0.97-d B and 3.57-d B when false alarm probability is 0.1, compared with a conventional trust-value-based algorithm and a trusted collaborative spectrum sensing for mobile CRNs, respectively.

A Novel Negative Multinomial Distribution Based Energy-Efficient Reputation Modeling for Wireless Sensor Networks(WSNs)

In wireless sensor networks(WSNs),nodes are usually powered by batteries.Since the energy consumption directly impacts the network lifespan,energy saving is a vital issue in WSNs,especially in the designing phase of cryptographic algorithms.As a complementary mechanism,reputation has been applied to WSNs.Different from most reputation schemes that were based on beta distribution,negative multinomial distribution was deduced and its feasibility in the reputation modeling was proved.Through comparison tests with beta distribution based reputation in terms of the update computation,results show that the proposed method in this research is more energy-efficient for the reputation update and thus can better prolong the lifespan of WSNs.