Reputation Model Research Articles

A DLT-based framework for secure IoT infrastructure in smart communities

In the field of smart communities, significant progress has been made in recent years. The objective of constructing smart communities is to improve the quality of life of their inhabitants. To accomplish this objective, technologies such as Internet of Things (IoT) and Artificial Intelligence (AI) were deployed. The data gathered and processed by IoT devices, particularly those with centralized control, are however susceptible to availability, integrity, and privacy risks. Due to its inherent properties of transparency, immutability, and underlying secure-by-design architecture, Distributed Ledger Technology (DLT) and Smart Contracts enable distributed, decentralized, automated workflows that can be incorporated to automate the management of the next generation of IoT networks. Using a potential use case, a conceptual architecture for securing smart communities with DLT is developed and explained. In this paper, a framework for IoT eco-systems is proposed that provides seamless integration between IoT and DLT to create a decentralized trusted architecture that ensures the trustworthiness of IoT eco-systems at design time and a trust reputation model based on the architecture to protect it at run-time. In addition, the initial implementation steps are described for this framework.

Linguistic Fuzzy Trust Model over Oscillating Wireless Sensor Networks

Simulation of the Linguistic Fuzzy Trust Model (LFTM) over oscillating Wireless Sensor Networks (WSNs) where the goodness of the servers belonging to them could change along the time is presented in this paper, and the comparison between the outcomes achieved with LFTM model over oscillating WSNs with the outcomes obtained by applying the model over static WSNs where the servers maintaining always the same goodness, in terms of the selection percentage of trustworthy servers (the accuracy of the model) and the average path length are also presented here. Also in this paper the comparison between the LFTM and the Bio-inspired Trust and Reputation Model for Wireless Sensor Networks (BTRM-WSN) in terms of the accuracy and the average path length suggested by each model is presented. Both models give quite good and accurate outcomes over oscillating WSNs. Also it must be mentioned that the evaluation environment used here is Trust and Reputation Model Simulator for WSN.

Dynamic Transaction Confirmation Sharding Protocol for Alliance Chain

Alliance chain has gained widespread popularity in industrial and commercial fields due to its multi-centralization and node manageability. Current implementations of the alliance chain suffer from scalability obstacles, such as communication congestion and throughput drop, when the number of nodes increases. In this paper, a novel dynamic transaction confirmation sharding protocol is proposed, which improves transaction processing efficiency by partitioning nodes and assigning different transactions to different shards. It utilizes dynamic transaction confirmation consensus as a sharding intra-consensus mechanism to minimize message size and package transactions into microblocks, which modifies communication content during transaction propagation among shards and reduces network congestion and shard reconfigure cost. The protocol leverages a review system and reputation model to identify and punish malicious nodes and also incorporates a verifiable random function for node configuration, which ensures a sufficient number of honest nodes within the shard and prevents repeated consensus processes. Simulation results show that the proposed protocol outperforms mainstream used permissioned chain sharding protocols Attested HyperLedger and Sharper, achieving a throughput improvement of at least 20%. This protocol is suitable for scenarios requiring high throughput and reliability in industrial and commercial fields such as finance, logistics, and supply chain management. Even if the number of alliance chain nodes increases to the usual maximum, or there are some faulty nodes, the protocol can still maintain stable performance.

$\mathcal {R}$-Tracing: Consortium Blockchain-Based Vehicle Reputation Management for Resistance to Malicious Attacks and Selfish Behaviors

Empowered by 5G communication technology, high-speed information exchange can be realized on the Internet of Vehicles (IoV) for future intelligent transportation applications. Since most applications require vehicles to report perceived information to nearby base stations, how to defend against malicious attacks and selfish behaviors of vehicles in reporting activities has become an essential security issue. Some researchers have attempted to encourage vehicles to actively report truthful information by establishing reputations for them, but these efforts have some shortcomings. In model design, they neither verify the basis and process of reputation evaluation nor pay attention to the diversity and intelligence of vehicle behaviors. In system construction, they not only fail to meet the management needs of multi-party participation but also fail to provide verifiable reputation management services. In this paper, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing, a consortium blockchain-based vehicle reputation management scheme. The main contribution of our work lies in three points. First, a vehicle reputation model is designed with a reward and punishment mechanism and a regular tax mechanism. Second, a vehicle reputation management system is constructed by using Hyperledger Fabric, in which all tasks of reputation value update are abstracted into three types of transactions. Finally, the effectiveness of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing is verified by extensive simulations running on a large-scale traffic scenario and performance evaluation on a prototype system. Compared with the typical linear reputation model, our model not only resists the usual malicious attack and selfish behavior but also effectively deals with the on-off attack and rational selfish behavior improved by intelligent vehicles. In the test of throughput, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal{R}$</tex-math></inline-formula> -tracing achieves 600/textittps, outperforming two state-of-the-art blockchain-based schemes.

Reputation and Partial Default

This paper presents a continuous-time reputation model of sovereign debt allowing for both varying levels of partial default and full default. In it, a government can be a nonstrategic commitment type or a strategic opportunistic type, and a government’s reputation is its equilibrium Bayesian posterior of being the commitment type. Our equilibrium has that for bond levels reachable by both types without defaulting, bigger partial defaults (or bigger haircuts for bond holders) imply higher interest rates for subsequent bond issuances, as in the data. (JEL D83, E32, E43, G12, H63)

Multiproduct firm’s reputation and leniency program in multimarket collusion

The corporate leniency program is believed to be useful for antitrust authorities. Our study challenges this belief in a setting where a large multiproduct firm meets single-product firms in independent and isolated markets. We explore an adversary effect of the corporate leniency program in multimarket collusion in a reputation model. A multiproduct firm forming cartels in multiple markets can build its reputation as a tough firm that punishes any deviation by applying for leniency. We show that the multiproduct firm can manipulate the leniency program to stabilize cartels in markets without material linkages (such as demand linkages). This effect does not exist if only one market exists or the leniency-application outcome is not publicly observable. Our findings theoretically explain why the numerous leniency applications by multiproduct firms should concern antitrust agencies and imply several directions for revising the leniency policy.

Reputation as Capital—How Decentralized Autonomous Organizations Address Shortcomings in the Venture Capital Market

Venture capital (VC) models can be optimized with emerging decentralized technology. There are many disadvantages that come with traditional VC fundraising including illiquidity and ownership struggles, as well as timing. This paper will discuss alternative funding mechanisms that may be available and advantageous to emerging businesses. After discussing the shortcomings of the existing VC market and the rise of alternative early round funding mechanisms, the paper highlights the evolution of VC businesses that are operated by a Decentralized Autonomous Organization (DAO). More specifically, models discussed in this article contribute to the much-needed experimentation with venture capital reputation models.

Robustness evaluation of trust and reputation systems using a deep reinforcement learning approach

One of the biggest challenges facing trust and reputation systems (TRSs) is evaluating their ability to withstand various attacks, as these systems are vulnerable to multiple types of attacks. While simulation methods have been used to evaluate TRSs, they are limited because they cannot detect new attacks and do not ensure the system’s overall robustness. To address this limitation, verification methods have been proposed that can examine the entire state space and detect all possible attacks. However, these methods are not always practical for large models and real environments because they suffer from the state space explosion problem. To tackle this issue, we propose a deep reinforcement learning approach for evaluating the robustness of TRSs. In this approach, an agent can learn how to attack a system and find the best attack plan without prior knowledge. Additionally, our method avoids the state space explosion problem because it uses a deep Q-network instead of storing and examining the entire state space. We tested our proposed method on five well-known reputation models, assessing various attack goals such as selfishness, maliciousness, competition, and slandering. The results showed that our method was successful in identifying the best attack plan and executing it successfully in the system, demonstrating its effectiveness in evaluating the robustness of TRSs.

An Improved Byzantine Fault-Tolerant Algorithm Based on Reputation Model

This study proposes an improved Byzantine fault-tolerant consensus RB-BFT based on the reputation model to address the problems of low reliability of primary nodes and high communication complexity in the practical Byzantine algorithm (PBFT). First, this algorithm establishes a dynamic reputation model of nodes to distinguish honest and malicious nodes in the system, lowering the likelihood of malicious nodes being chosen as primary nodes and increasing the dependability of primary nodes. Second, the algorithm introduces supervisory nodes for information supervision while reducing the centrality of the system. Finally, this algorithm improves the consistency protocol of the PBFT algorithm by optimizing the process of mutual communication between nodes in the preparation and commitment phases, which reduces the algorithm communication complexity from O(n2) to O(n). Theoretical and practical studies reveal that the RB-BFT algorithm enhances performance and reliability greatly.

Actions speak louder than words: Imputing users’ reputation from transaction history

The choice of market mechanism is key to success for any online marketplace. In recent years, as peer‐to‐peer (P2P) lending has seen phenomenal growth, leading P2P lending platforms have used various market mechanisms and, in some cases, even switched from one mechanism to another, chasing higher market share and overall growth. While Prosper.com, a leading P2P lending platform, has switched from the auction lending model to a fixed price lending model, recent studies show that overall social welfare was higher with the auction lending model. While the auction lending model gives more power to the lenders, the success of the auction lending model hinges on the accuracy of lenders’ assessment of the credit risk of the borrowers. Building on extant literature and in support of the auction lending model to increase social welfare, we design an artifact to dynamically estimate borrower reputation to help the lenders and improve the allocative efficiency in P2P lending markets. We posit that borrowers’ reputation built on transactional data, readily available on P2P lending platforms, represents the collective perception of the lenders about the borrowers. We propose a dynamic latent class model of reputation and use the latent instrumental variable approach to deal with endogeneity. We test our artifact using real‐world P2P lending data. We show that accounting for reputation improves the model's explanatory power and provides a way to empirically model the evolution and impact of reputation in online platforms where repeated transactions are performed.

A player reputation system based on belief formation among Non-Player character societies in Open-World Role-Playing games

The present work is an attempt to design and implement an artificial game environment that provides information dissemination among Non-Player Characters (NPCs) of Open-World Role-Playing Game communities. Based on the perspective that an NPC is a mobile, communicating, cooperative agent that has autonomy and learning ability, and shows socialness in its virtual environment, it was possible to experiment and observe the impacts of human player-generated events on NPCs’ social learning. By means of introducing different tuneable NPC autonomy and socialness parameter values, it is ensured that information and NPC opinions about the player not only spread over the society, but they also help develop player reputation among NPCs.The defined primitives such as NPCs’ self and group-model views; event valuation and reputation models; established learning, memorization, forgetting mechanisms; the introduced information exchange and update protocol; and reputation metric provided for the construction of a tuneable, scalable virtual environment that can be used to investigate the individual and social behavioural aspects of artificial NPC societies. In this virtual environment, it is shown that player’s reputation converges to fixed values. This is noteworthy since it is an indicator of societal learning and belief formation through NPC communications in the game environment.

Linking Informative and Factual CSR Communication to Reputation: Understanding CSR Motives and Organizational Identification

The way corporate social responsibility (CSR) communication relates to corporate reputation has attracted an increasing amount of attention from communication and business researchers and practitioners. To place our study in the context of CSR and employee communication, we proposed a CSR communication—motives—organizational identification—corporate reputation model. Data collected from an online Qualtrics survey (n = 811) supported all the proposed hypotheses linking informativeness and factual tone in CSR communication, employee-perceived intrinsic/other-serving motives of their organizations’ CSR activities, organizational identification, and corporate reputation. Specifically, informativeness and a factual tone in CSR communication were positively related to employee-perceived intrinsic/other-serving motives of their organizations’ CSR activities. Employee-perceived intrinsic/other-serving motives of their organizations’ CSR activities were positively associated with employee organizational identification. Employee organizational identification was positively related to corporate reputation. In addition, employee-perceived intrinsic/other-serving motives of their organizations’ CSR activities and employee organizational identification turned out to be two significant mediators in the proposed model between CSR communication and corporate reputation. We conducted a two-step structural equation modeling (SEM) analysis to analyze the collected data. The theoretical and practical implications of the study were discussed.

Crisis Bargaining in the Shadow of Third-Party Opportunism

Abstract The prospect of a rival opportunistically pressing for gains while one is at war with another rival highly influenced Britain's “two-power standard” as well as the US's “two-war standard.” Conflict scholars have documented numerous instances of third-party opportunism. I analyze a game-theoretic model of crisis bargaining in the shadow of third-party opportunism. Under complete information, a country with multiple rivals, that lacks overwhelming military power, incurs bargaining vulnerabilities with each rival due to potential opportunism by the others. Under incomplete information, uncertainty across dyads can lead to war even if there is complete information within dyads, adding more uncertainty to the interaction can actually reduce the likelihood of war, resolve across disputes can be endogenously correlated in equilibrium (in contrast to the exogenous assumption of reputational models), and a commitment problem can be conducive to peace rather than war.

Sustainability of Stack Exchange Q&A communities: the role of trust

Knowledge-sharing communities are fundamental elements of a knowledge-based society. Understanding how different factors influence their sustainability is of crucial importance. We explore the role of the social network structure and social trust in their sustainability. We analyze the early evolution of social networks in four pairs of active and closed Stack Exchange communities on topics of physics, astronomy, economics, and literature and use a dynamical reputation model to quantify the evolution of social trust in them. In addition, we study the evolution of two active communities on mathematics topics and two closed communities about startups and compare them with our main results. Active communities have higher local cohesiveness and develop stable, better-connected, trustworthy cores. The early emergence of a stable and trustworthy core may be crucial for sustainable knowledge-sharing communities.

Cutting-Edge Assets for Trust in 5G and Beyond: Requirements, State of the Art, Trends, and Challenges

In 5G and beyond, the figure of cross-operator/domain connections and relationships grows exponentially among stakeholders, resources, and services, with reputation-based trust models being one of the capital technologies leveraged for trustworthy decision-making. This work studies novel 5G assets on which trust can be used to overcome unsuitable decision-making and address current requirements. First, it introduces a background and general architecture of reputation-based trust models. Then, it analyzes pivotal 5G assets on which trust can enhance their performance. In addition, this article performs a comprehensive review of the current reputation models applied to 5G assets and compares their properties, features, techniques, and results. Finally, it provides current trends and future challenges to conducting forthcoming research in the area.

Reputation-Aware Multi-Agent DRL for Secure Hierarchical Federated Learning in IoT

Aiming at protecting device data privacy, Federated Learning (FL) is a framework of distributed machine learning in which devices’ local model parameters are exchanged with a centralized server without revealing the actual data. Hierarchical Federated Learning (HFL) framework was introduced to improve FL communication efficiency where devices are clustered and seek model consensus with the support of edge servers (e.g., base stations). Devices in a cluster submit their local model updates to their assigned local edge server for aggregation at each iteration. The edge servers transmit the aggregated models to a centralized server and establish a global consensus. However, similar to FL, adversaries may threaten the security and privacy of HFL. The client devices within a cluster may deliberately provide unreliable local model updates through poisoning attacks or poor-quality model updates due to inconsistent communication channels, increased device mobility, or inadequate device resources. To address the above challenges, this paper investigates the client selection problem in the HFL framework to eliminate the impact of unreliable clients while maximizing the global model accuracy of HFL. Each FL edge server is equipped with a Deep Reinforcement Learning (DRL)-based reputation model to optimally measure the reliability and trustworthiness of FL workers within its cluster. A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is utilized to enhance the accuracy and stability of the HFL global model, given the workers’ dynamic behaviors in the HFL environment. The experimental results indicate that our proposed MADDPG improves the accuracy and stability of HFL compared with the conventional reputation model and single-agent DDPG-based reputation model.

Deep-Learning based Reputation Model for Indirect Trust Management

In the digital era, human and thing behavioral patterns have been merged, which leads to the need for trust management to secure the relationship among people and things (e.g., driverless cars). Due to the dynamism and complexity of digital environments, trust management depends largely on indirect trust to support its reasoning by building the reputation of trustees based on recommendations reflected in the feedback of sentiment and non-sentiment objects. However, different biases are still affecting the accuracy of indirect trust that reflects a collective trustworthiness belief or societal stereotypes. This work focuses on enabling indirect trust management by leveraging deep learning in combination with synthetic data for bias management. Specifically, this paper proposes a reputation model to support decision-making in trust management by minimizing bias in indirect trust information and fostering fairly the relationship among sentiment and non-sentiment objects. Our experimental results show that the synthetic data can significantly improve the classification accuracy in trust management.

Quantifying a State's Reputation in the Strategic Competition and Crisis Wargame for the Center for Army Analysis

The Center for Army Analysis (CAA) developed the Strategic Competition and Crisis (SC2) wargame to capture how modern military competition operates in today’s world. The CAA tasked our interdisciplinary team with developing a more robust, well-rounded reputation model for quantifying a state’s reputation based on another state’s perspective using each of our respective specialties within systems engineering, defense and strategic studies, and operations research. The team leveraged tools within the Systems Decision Process (SDP) to quantify the theoretical, intangible concept of reputation. Research began with a qualitative value model based on expert stakeholder analysis and a literature review. The team then identified value measures to build a swing weight matrix that produced a reputation score for each state from another state’s perspective. The concept of swing weighting incorporates prioritized value measures while accounting for variance in the raw data that is used to aggregate a score for each country’s reputation level. That score, alongside an enhanced Game User Interface (GUI), can now be integrated into the existing SC2 wargame to provide a more complete, narrative experience that charts player decisions throughout the game and educates senior Army leaders on how to effectively leverage military activities towards achieving strategic goals.

Improvement of the PBFT Algorithm Based on Grouping and Reputation Value Voting

An improved practical Byzantine fault tolerance (Practical Byzantine Fault Tolerant consensus algorithm based on reputation, RPBFT) algorithm based on grouping and reputation value voting is proposed for the problems of high communication complexity, poor scalability, and random selection of master nodes of the practical Byzantine fault tolerance (PBFT) consensus algorithm of the consortium chain. First, the consistency process is optimized to take the response speed of nodes to each group leader as the basis of grouping, and the intragroup consensus is performed. The group leader then takes the result of intragroup consensus and participates in extra-group consensus to reduce the frequency and time of internode communication. Second, the reputation model and voting mechanism are proposed, and the group leader is generated by node reputation value voting, which enhances the initiative and reliability of trusted nodes and reduces the abnormal nodes as group leader. Finally, a simulation and performance testing system based on this improved scheme is built to prove the effectiveness as well as the usability of the scheme through simulation experiments. The experimental results show that when the number of network nodes is 36, the throughput of the RPBFT algorithm is six times that of PBFT. Therefore, the consensus delay is reduced by 91.7%, and the communication overhead is reduced by 37.8%.

A secure and efficient multi-domain data sharing model on consortium chain

In a large-scale networking scenario with massive distribution of devices, data are independently generated and maintained by multiple domains. To solve the problem of isolated data islands in multi-domain, this paper designs a multi-domain data sharing model based on consortium chain. Aiming at the problems of low efficiency and lack of consideration of intra-domain consensus processes in traditional data sharing schemes, this paper optimizes the security and efficiency of data sharing from two parts: inter-domain chain and intra-domain chain. To ensure fine granularity, flexibility and security of the access control process in inter-domain chain, this model combines the Attribute-Based Access Control (ABAC) with smart contract, and a permission grading mechanism is designed to solve the problem of low retrieval efficiency in this access control model through reducing the retrieval range by grading and matching Policy set. Considering the problem of high delay of node consensus in the large-scale networking environment of intra-domain chain, a layered practical Byzantine fault tolerance optimization (LPBFT) algorithm with introducing a reputation model is designed, which greatly improves the efficiency and security of the intra-domain consensus. The consortium chain model is designed and implemented from the perspectives of inter-domain and intra-domain, which optimized the security and efficiency of the multi-domain data sharing model. Finally, a prototype system is implemented based on Hyperledger Fabric, and the validity of the model is verified from the aspects of inter-domain access control and intra-domain consensus algorithm.