Distributed Reputation System Research Articles

RBT: A distributed reputation system for blockchain-based peer-to-peer energy trading with fairness consideration

It has been proved by recent works that reputation can help to improve the efficiency of blockchain and enhance the fairness of energy trading markets. However, the application of distributed reputation in the energy field has not been fully studied yet. In this paper, we design a distributed reputation system to simulate real-world trust in blockchain-based peer-to-peer energy trading. It is a comprehensive reputation system in the sense that reputation scores are decided by the behavior of participants as consensus nodes, energy buyer, and energy sellers. Its implementation relies on blockchain, especially the smart contract technology, to achieve distributed and automatic reputation management. The distributed reputation system in turn helps to implement a delegated consensus algorithm for blockchain and a reputation-based k-double auction matchmaking scheme for peer-to-peer energy trading. In addition, we define a fairness indicator to capture the reputation-based average benefits and costs when considering reputation as the contribution to the peer-to-peer energy trading market. By simulating the comprehensive system, the numerical results demonstrate the effects of distributed reputation in improving the efficiency of blockchain and balancing fairness indicators between sellers and buyers during peer-to-peer energy trading. As far as we know, this paper is one of the few works to provide a formal method of evaluating the fairness of the peer-to-peer energy trading market.

A fully distributed reputation system for m-commerce via ad hoc wireless networking

A fully distributed reputation system for m-commerce via ad hoc wireless networking

Causal Modelling and Analysis Evaluation of Online Reputation Management Using Fuzzy Delphi and DEMATEL

Online reputation management (ORM) is a significant and proactive tool that can reinforce the credibility of the service provider. Literature existing today on this topic has rarely reported on the causal modeling analysis from an ORM perspective. Therefore, the objective of this paper is to build a factor structure of ORM and to build the inter-relationship map amongst the criteria of each factor. To allow for vague human judgment, a fuzzy concept is employed in a form of Fuzzy Delphi. The DEMATEL technique has been used to develop a Network Relationship Map (NRM) among the criteria of each factor. Data has been gathered through a structured questionnaire conducted with a survey of experts. The study divided the criteria of each factor into cause-effect criteria. Findings of the study show that criteria such as distributed reputation system, trust, online competitive branding, website management, customer relationship, search engine optimization, corporate social responsibility, users' reach, competition/page views, purchase discounted products and cash back or money back fall under the cause group of ORM's factors. The results of this study can not only help service providers to enhance their reputation but can also guide them towards targeting their customers in an online platform.

Fighting pollution attacks in P2P streaming

In recent years, the demand for multimedia streaming over the Internet is soaring. Due to the lack of a centralized point of administration, Peer-to-Peer (P2P) streaming systems are vulnerable to pollution attacks, in which video segments might be altered by any peer before being shared. Among existing proposals, reputation-based defense mechanisms are the most effective and practical solutions. We performed a measurement study on the effectiveness of this class of solutions. We implemented a framework that allows us to simulate different variations of the reputation rating systems, from the centralized global approaches to the decentralized local approaches, under different parameter settings and pollution models. One key finding is that a centralized reputation system is only effective in static network and in defending against light pollution attacks. In general, a fully distributed reputation system is more suitable for the “real-time” P2P streaming system, since it is better in handling network dynamics and fast in detecting the polluters. Based on this key finding, we propose DRank, a fully distributed rank-based reputation system. Experimental results show that this technique is more flexible and robust in fighting pollution attacks.

Multi-party trust computation in decentralized environments in the presence of malicious adversaries

In this paper, we describe a decentralized privacy-preserving protocol for securely casting trust ratings in distributed reputation systems. Our protocol allows n participants to cast their votes in a way that preserves the privacy of individual values against both internal and external attacks. The protocol is coupled with an extensive theoretical analysis in which we formally prove that our protocol is resistant to collusion against as many as n−1 corrupted nodes in both the semi-honest and malicious adversarial models.The behavior of our protocol is tested in a real P2P network by measuring its communication delay and processing overhead. The experimental results uncover the advantages of our protocol over previous works in the area; without sacrificing security, our decentralized protocol is shown to be almost one order of magnitude faster than the previous best protocol for providing anonymous feedback.

SimplyRep: A simple and effective reputation system to fight pollution in P2P live streaming

Peer-to-Peer (P2P) streaming has become a popular platform for transmitting live content. However, due to their increasing popularity, P2P live streaming systems may be the target of user opportunistic actions and malicious attacks, which may greatly reduce streaming rate or even stop it completely. In this article, we focus on a specific type of attack called content pollution, in which malicious peers tamper or forge media data, introducing fake content before uploading it to their partners in the overlay network. Specifically, we present a new decentralized reputation system, named SimplyRep, that quickly identifies and penalizes content polluters, while incurring in low overhead in terms of bandwidth consumption. We evaluate our method with both simulation and experiments in PlanetLab, comparing it against two previously proposed approaches, namely, a centralized black list and a distributed reputation system, in various scenarios. Our results indicate that SimplyRep greatly outperforms the two alternatives considered. In particular, both black list and the distributed reputation method perform poorly when polluters act jointly in a collusion attack, reaching a data retransmission overhead (triggered by polluted chunks received) of 70% and 30%, respectively, whereas the overhead experienced by SimplyRep is at most 2%. Our results also show that SimplyRep is able to quickly isolate almost all polluters under a dissimulation attack, being also somewhat robust to a whitewashing attack, although the latter remains a challenge to effective P2P streaming.

Online reputation management for improving marketing by using a hybrid MCDM model

Online reputation management (ORM) has been considered as a significant tool of internet marketing. The purpose of this paper is to construct a decision model for evaluating performances and improving professional services of marketing. To investigate the interrelationship and influential weights among criteria, this study uses a hybrid MCDM model including decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based analytic network process (called DANP). The empirical findings reveal that criteria have self-effect relationships based on DEMATEL technique. According to the network relation map (NRM), the dimension that professional services of marketing should improve first when carrying out ORM is online reputation. In the five criteria for evaluation, distributed reputation systems is the most important criterion impacting ORM, followed by employees and social responsibility.

Improving social odometry robot networks with distributed reputation systems for collaborative purposes.

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.

The Frog-Boiling Attack

A network coordinate system assigns Euclidean “virtual” coordinates to every node in a network to allow easy estimation of network latency between pairs of nodes that have never contacted each other. These systems have been implemented in a variety of applications, most notably the popular Vuze BitTorrent client. Zage and Nita-Rotaru (at CCS 2007) and independently, Kaafar et al. (at SIGCOMM 2007), demonstrated that several widely-cited network coordinate systems are prone to simple attacks, and proposed mechanisms to defeat these attacks using outlier detection to filter out adversarial inputs. Kaafar et al. goes a step further and requires that a fraction of the network is trusted. More recently, Sherr et al. (at USENIX ATC 2009) proposed Veracity, a distributed reputation system to secure network coordinate systems. We describe a new attack on network coordinate systems, Frog-Boiling, that defeats all of these defenses. Thus, even a system with trusted entities is still vulnerable to attacks. Moreover, having witnesses vouch for your coordinates as in Veracity does not prevent our attack. Finally, we demonstrate empirically that the Frog-Boiling attack is more disruptive than the previously known attacks: systems that attempt to reject “bad” inputs by statistical means or reputation cannot be used to secure a network coordinate system.

DHTrust: a robust and distributed reputation system for trusted peer‐to‐peer networks

SUMMARYThe anonymity and dynamic character of a Peer‐to‐Peer (P2P) network makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT (Distributed Hash Table) trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the DHT to distribute local reputation to trade‐off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in the P2P network, i.e. providing service and issuing reputation scores. To adapt to the dynamic P2P networks, we take dynamic node mechanism into account. Our scheme can assure convergence effectiveness and robustness, when nodes enter or leave the system. We conduct extensive simulations to evaluate the performance of DHTrust. The results show that our system makes significant improvement in convergence speed and aggregation accuracy. Moreover, it is robust to malicious peers. Copyright © 2011 John Wiley & Sons, Ltd.

Reputation-Enabled Self-Modification for Target Sensing in Wireless Sensor Networks

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Wireless sensor networks provide new tools for sensing physical environments. However, the general existence of faulty sensor measurements in networks will cause degradation of the network service quality and huge burden of the precious energy. While cryptography-based approaches are helpless of information generation, reputation systems are demonstrated of positive results. In this paper, we investigate the benefits of a distributed reputation system in target localization. A node reputation is defined as its measurement performance and is computed by the Dirichlet distribution. By assuming the sensing model of each node to be mixed Gaussian, we use reputation to estimate parameters of the sensing model and modify a node's original measurement. We also develop a reputation-based local voting algorithm to filter the untrustworthy data and then estimate the target location by a particle swarm optimization algorithm. To assure energy efficiency of the proposed approach, we use a reputation-based model to indicate the information importance of each data packet and ensure that a more important packet can be delivered with higher reliability. Finally, we experimentally evaluate the reputation system and demonstrate its accuracy and energy efficiency. </para>

On Desideratum for B2C E-Commerce Reputation Systems

This paper reviews existing approaches to reputation systems, their constraints as well as available solutions. Furthermore, it presents and evaluates a novel and comprehensive reputation model devoted to the distributed reputation system for Business-to-Consumer (B2C) E-commerce applications that overcomes the discussed drawbacks. The algorithm offers a comprehensive approach as considers a number of issues that have a bearing on trust and reputation such as age of ratings, transaction value, credibility of referees, number of malicious incidents, collusion and unfair ratings. Moreover, it also extends the existing frameworks based on information about past behaviour, with other aspects affecting online trading decisions which relate to the characteristic of the providers, such as existence of trust mark seals, payment intermediaries, privacy statements, security/privacy strategies, purchase protection/insurance, alternative dispute resolutions as well as the existence of first party information.

RRM: An incentive reputation model for promoting good behaviors in distributed systems

Reputation systems represent soft security mechanisms that complement traditional information security mechanisms. They are now widely used in online e-commerce markets and communities in order to stimulate good behaviors as well as to restrain adverse behaviors. This paper analyzes the limitations of the conversational reputation models and proposes an incentive reputation model called the resilient reputation model (RRM) for the distributed reputation systems. The objective of this reputation model is not only to encourage the users to provide good services and, therefore, to maximize the probability of good transaction outcomes, but also to punish those adverse users who are trying to manipulate the application systems. The simulation results indicate that the proposed reputation model (RRM) could effectively resist against the common adverse behaviors, while protecting the profits of sincere users from being blemished by those adversaries.