Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a...Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a bottleneck to the widespread adoption of UCAN in 6G.In this paper,we propose Overlap Shard,a blockchain framework based on a novel reputation voting(RV)scheme,to dynamically manage the APs in UCAN.AP nodes in UCAN are distributed across multiple shards based on the RV scheme.That is,nodes with good reputation(virtuous behavior)are likely to be selected in the overlap shard.The RV mechanism ensures the security of UCAN because most APs adopt virtuous behaviors.Furthermore,to improve the efficiency of the Overlap Shard,we reduce cross-shard transactions by introducing core nodes.Specifically,a few nodes are overlapped in different shards,which can directly process the transactions in two shards instead of crossshard transactions.This greatly increases the speed of transactions between shards and thus the throughput of the overlap shard.The experiments show that the throughput of the overlap shard is about 2.5 times that of the non-sharded blockchain.展开更多
With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)of...With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure,thereby reducing the computational burden on connected vehicles.However,this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes.Existing vehicular edge computing platforms have not adequately considered themisbehavior of vehicles.We propose a practical task offloading algorithm based on reputation assessment to address the task offloading problem in vehicular edge computing under an unreliable environment.This approach integrates deep reinforcement learning and reputation management to address task offloading challenges.Simulation experiments conducted using Veins demonstrate the feasibility and effectiveness of the proposed method.展开更多
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 theor...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.展开更多
Volunteered geographic information(VGI)has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations.But the issue of deciding the ...Volunteered geographic information(VGI)has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations.But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge.This article first formulates the problem of quality assessment of VGI data.Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric,qualitative,and semantic aspects of edits over time.An implementation of the model is running on a small data-set for a preliminary empirical validation.The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.展开更多
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 proble...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.展开更多
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. Th...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.展开更多
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-A...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.展开更多
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 considerati...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.展开更多
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 c...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.展开更多
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...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.展开更多
Reputation mechanism has been recently introduced into wireless sensor networks(WSNs) to overcome the deficiencies incurred by cryptography alone.Unlike most current reputation mechanisms that are based on binomial di...Reputation mechanism has been recently introduced into wireless sensor networks(WSNs) to overcome the deficiencies incurred by cryptography alone.Unlike most current reputation mechanisms that are based on binomial distribution and to some extent,do not give due attention to the aspect of energy constraint of WSNs,this research deduces and proves the feasibility that negative binomial distribution can well be used in the reputation update with distinctive energy-saving features.Comparison tests with respect to the energy consumption in terms of reputation computing frequencies are done between the traditional reputation method and the one in this study.Results show that our method can save more energy for the reputation update and thus can better meet the power constraints of WSNs.展开更多
It is well known that the reputation is the basis of a seller to survive and gain trust from customers in a competitive business environment. But as the existence of information asymmetry between buyer and seller, the...It is well known that the reputation is the basis of a seller to survive and gain trust from customers in a competitive business environment. But as the existence of information asymmetry between buyer and seller, the moral hazard problem is the key obstacle that impedes the benefits of related shareholders and reduces the efficiency of total market. It is crucial to design a control mechanism to avoid the negative impact of moral hazard. This paper studies the principal and agent relationship between buyer and seller in C2C e-market;because of the influence of information asymmetry, many customers suffered from being cheated by sellers with defective products in practice. These frequent cases will deteriorate long term relationship between sellers and buyers. Here we focus on the analysis of the causes of moral risks and the effect of reputation on oral risk with repeated game theory. The purpose of this paper is to help both firms and customers effectively avoid morality risk and realize a win-win situation.展开更多
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 overh...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.展开更多
Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute....Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.展开更多
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...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.展开更多
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...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.展开更多
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 manage...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.展开更多
This article investigates the impact of CEO attributes on corporate reputation,financial performance,and corporate sustainable growth in India.Using static panel data methodology for a sample of NSE listed leading 138...This article investigates the impact of CEO attributes on corporate reputation,financial performance,and corporate sustainable growth in India.Using static panel data methodology for a sample of NSE listed leading 138 non-financial companies over the time-frame 2011 to 2018,we find that CEO remuneration and tenure maintains significant positive associations with corporate reputation,while duality and CEO busyness are found to be associated with corporate reputation negatively.The results also show that female CEOs and CEO remuneration are associated with corporate financial performance positively,whereas CEO busyness,as expected,holds a significant negative relationship with corporate financial performance.Moreover,the results demonstrate that CEO age is associated with corporate sustainable growth negatively,while tenure appears to have a significant and positive association with corporate sustainable growth.The results are robust to various tests and suggest that in the Indian context,demographic and job-specific attributes of CEOs exert significant influence on corpo-rate reputation,financial performance,and corporate sustainable growth.The empirical findings would provide a basis for the shareholders and companies to identify areas of consideration when appointing CEOs and determining their roles and responsibilities.展开更多
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 ...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.展开更多
针对Reputation机制中缺乏对高信誉值节点的奖励,以及Credit机制中静止的虚拟银行(VB:Virtual Bank)部署困难,同时交易价格无区分性等导致的激励效果不佳的问题,文中提出了一种新的基于Reputation和Credit的混合激励算法RCH(Reputation ...针对Reputation机制中缺乏对高信誉值节点的奖励,以及Credit机制中静止的虚拟银行(VB:Virtual Bank)部署困难,同时交易价格无区分性等导致的激励效果不佳的问题,文中提出了一种新的基于Reputation和Credit的混合激励算法RCH(Reputation and Credit based Hybrid incentive algorism)。RCH主要是采取一种P2P的模式(无需VB),根据提供转发服务节点与消息目的节点的亲密度以及接受转发服务节点的自私度来完成对不同服务质量,不同服务对象的区别定价,从而鼓励理性地自私节点更多的去帮助其他节点转发消息。通过理论分析和仿真实验得出:该算法与Reputation机制和Credit机制相比,在激励效果方面得到了较大的改善,最终都体现在交付率,平均延迟等指标的优化上。展开更多
基金supported by the National Natural Science Foundation of China(NSFC)under Grant 61931005.
文摘Blockchain-based user-centric access network(UCAN)fails in dynamic access point(AP)management,as it lacks an incentive mechanism to promote virtuous behavior.Furthermore,the low throughput of the blockchain has been a bottleneck to the widespread adoption of UCAN in 6G.In this paper,we propose Overlap Shard,a blockchain framework based on a novel reputation voting(RV)scheme,to dynamically manage the APs in UCAN.AP nodes in UCAN are distributed across multiple shards based on the RV scheme.That is,nodes with good reputation(virtuous behavior)are likely to be selected in the overlap shard.The RV mechanism ensures the security of UCAN because most APs adopt virtuous behaviors.Furthermore,to improve the efficiency of the Overlap Shard,we reduce cross-shard transactions by introducing core nodes.Specifically,a few nodes are overlapped in different shards,which can directly process the transactions in two shards instead of crossshard transactions.This greatly increases the speed of transactions between shards and thus the throughput of the overlap shard.The experiments show that the throughput of the overlap shard is about 2.5 times that of the non-sharded blockchain.
基金supported by the Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness(No.HNTS2022020)the Science and Technology Research Program of Henan Province of China(232102210134,182102210130)Key Research Projects of Henan Provincial Universities(25B520005).
文摘With the development of vehicle networks and the construction of roadside units,Vehicular Ad Hoc Networks(VANETs)are increasingly promoting cooperative computing patterns among vehicles.Vehicular edge computing(VEC)offers an effective solution to mitigate resource constraints by enabling task offloading to edge cloud infrastructure,thereby reducing the computational burden on connected vehicles.However,this sharing-based and distributed computing paradigm necessitates ensuring the credibility and reliability of various computation nodes.Existing vehicular edge computing platforms have not adequately considered themisbehavior of vehicles.We propose a practical task offloading algorithm based on reputation assessment to address the task offloading problem in vehicular edge computing under an unreliable environment.This approach integrates deep reinforcement learning and reputation management to address task offloading challenges.Simulation experiments conducted using Veins demonstrate the feasibility and effectiveness of the proposed method.
文摘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.
文摘Volunteered geographic information(VGI)has entered a phase where there are both a substantial amount of crowdsourced information available and a big interest in using it by organizations.But the issue of deciding the quality of VGI without resorting to a comparison with authoritative data remains an open challenge.This article first formulates the problem of quality assessment of VGI data.Then presents a model to measure trustworthiness of information and reputation of contributors by analyzing geometric,qualitative,and semantic aspects of edits over time.An implementation of the model is running on a small data-set for a preliminary empirical validation.The results indicate that the computed trustworthiness provides a valid approximation of VGI quality.
基金supported by the Program for Changjiang Scholars and Innovative Research Team in University under Grant No.IRT1078the Key Program of NSFC-Guangdong Union Foundation under Grant No.U1135002+1 种基金Major National S&T Program under Grant No.2011ZX03005-002the Fundamental Research Funds for the Central Universities under Grant No.JY10000903001
文摘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.
基金Supported by the National Natural Science Foun-dation of China (60173026) the Ministry of Education Key Project(105071) Foundation of E-Institute of Shanghai HighInstitutions(200301)
文摘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.
基金This work is supported by the Key Research and Development Project of Sichuan Province(No.2021YFSY0012,No.2020YFG0307,No.2021YFG0332)the Key Research and Development Project of Chengdu(No.2019-YF05-02028-GX)+1 种基金the Innovation Team of Quantum Security Communication of Sichuan Province(No.17TD0009)the Academic and Technical Leaders Training Funding Support Projects of Sichuan Province(No.2016120080102643).
文摘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.
基金supported by National Natural Science Foundation of China under Grant No. 61671183the Open Research Fund of State Key Laboratory of Space-Ground Integrated Information Technology under Grant No. 2015_SGIIT_KFJJ_TX_02major consulting projects of Chinese Academy of Engineering under Grant No. 2016-ZD-05-07
文摘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.
基金National Natural Science Foundations of China (No.61073177,60905037)
文摘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.
文摘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.
基金the National Natural Science Foundation of China(No.60905037)
文摘Reputation mechanism has been recently introduced into wireless sensor networks(WSNs) to overcome the deficiencies incurred by cryptography alone.Unlike most current reputation mechanisms that are based on binomial distribution and to some extent,do not give due attention to the aspect of energy constraint of WSNs,this research deduces and proves the feasibility that negative binomial distribution can well be used in the reputation update with distinctive energy-saving features.Comparison tests with respect to the energy consumption in terms of reputation computing frequencies are done between the traditional reputation method and the one in this study.Results show that our method can save more energy for the reputation update and thus can better meet the power constraints of WSNs.
文摘It is well known that the reputation is the basis of a seller to survive and gain trust from customers in a competitive business environment. But as the existence of information asymmetry between buyer and seller, the moral hazard problem is the key obstacle that impedes the benefits of related shareholders and reduces the efficiency of total market. It is crucial to design a control mechanism to avoid the negative impact of moral hazard. This paper studies the principal and agent relationship between buyer and seller in C2C e-market;because of the influence of information asymmetry, many customers suffered from being cheated by sellers with defective products in practice. These frequent cases will deteriorate long term relationship between sellers and buyers. Here we focus on the analysis of the causes of moral risks and the effect of reputation on oral risk with repeated game theory. The purpose of this paper is to help both firms and customers effectively avoid morality risk and realize a win-win situation.
基金ACKNOWLEDGEMENT This work was partially supported by the Na- tional Natural Science Foundation of China under Grant No. 61071127 and the Science and Technology Department of Zhejiang Pro- vince under Grants No. 2012C01036-1, No. 2011R10035.
文摘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.
文摘Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.
文摘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.
基金This work is supported by National Natural Science Foundation of China(Nos.U21A20463,62172117,61802383)Research Project of Pazhou Lab for Excellent Young Scholars(No.PZL2021KF0024)Guangzhou Basic and Applied Basic Research Foundation(Nos.202201010330,202201020162,202201020221).
文摘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.
基金supported by NSFC under Grant No.62341102National Key R&D Program of China under Grant No.2018YFA0701604.
文摘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.
文摘This article investigates the impact of CEO attributes on corporate reputation,financial performance,and corporate sustainable growth in India.Using static panel data methodology for a sample of NSE listed leading 138 non-financial companies over the time-frame 2011 to 2018,we find that CEO remuneration and tenure maintains significant positive associations with corporate reputation,while duality and CEO busyness are found to be associated with corporate reputation negatively.The results also show that female CEOs and CEO remuneration are associated with corporate financial performance positively,whereas CEO busyness,as expected,holds a significant negative relationship with corporate financial performance.Moreover,the results demonstrate that CEO age is associated with corporate sustainable growth negatively,while tenure appears to have a significant and positive association with corporate sustainable growth.The results are robust to various tests and suggest that in the Indian context,demographic and job-specific attributes of CEOs exert significant influence on corpo-rate reputation,financial performance,and corporate sustainable growth.The empirical findings would provide a basis for the shareholders and companies to identify areas of consideration when appointing CEOs and determining their roles and responsibilities.
基金the National Natural Science Foundation of China(60573043)the Natural Science Foundation of Guangdong Province(06025838)
文摘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.
文摘针对Reputation机制中缺乏对高信誉值节点的奖励,以及Credit机制中静止的虚拟银行(VB:Virtual Bank)部署困难,同时交易价格无区分性等导致的激励效果不佳的问题,文中提出了一种新的基于Reputation和Credit的混合激励算法RCH(Reputation and Credit based Hybrid incentive algorism)。RCH主要是采取一种P2P的模式(无需VB),根据提供转发服务节点与消息目的节点的亲密度以及接受转发服务节点的自私度来完成对不同服务质量,不同服务对象的区别定价,从而鼓励理性地自私节点更多的去帮助其他节点转发消息。通过理论分析和仿真实验得出:该算法与Reputation机制和Credit机制相比,在激励效果方面得到了较大的改善,最终都体现在交付率,平均延迟等指标的优化上。
