An Evaluation Framework for Reputation Management Systems

Reputation management (RM) is employed in distributed and peer-to-peer networks to help users compute a measure of trust in other users based on initial belief, observed behavior, and run-time feedback. These trust values influence how, or with whom, a user will interact. Existing literature on RM focuses primarily on algorithm development, not comparative analysis. To remedy this, we propose an evaluation framework based on the trace-simulator paradigm. Trace file generation emulates a variety of network configurations, and particular attention is given to modeling malicious user behavior. Simulation is trace-based and incremental trust calculation techniques are developed to allow experimentation with networks of substantial size. The described framework is available as open source so that researchers can evaluate the effectiveness of other reputation management techniques and/or extend functionality. This chapter reports on our framework’s design decisions. Our goal being to build a general-purpose simulator, we have the opportunity to characterize the breadth of existing RM systems. Further, we demonstrate our tool using two reputation algorithms (EigenTrust and a modified TNA-SL) under varied network conditions. Our analysis permits us to make claims about the algorithms’ comparative merits. We conclude that such systems, assuming their distribution is secure, are highly effective at managing trust, even against adversarial collectives

Facet Based Estimation Polling From Customer Reviews

Reputation-based belief systems are broadly used in e-Trade applications, and response ratings are aggregated to figure out traders’ reputation grades. The “all good reputation” problem, however, is prevalent in recent reputation systems.  Reputation grades across the web are commonly high for traders and it is difficult for potential customers to choose accurate traders.  This work is based on the observation that customers often express their viewpoints explicitly in free text response comments. We propose a system that figure out Comm-Trust for trust evaluation by drilling response comments. We propose multidimensional belief model for estimating reputation grades from user response comments. We propose an algorithm for mining response comments for dimension ratings and weights, combing techniques of natural language processing, opinion mining and topic modeling. This research work is mainly based on the first piece of work on trust evaluation by mining response comments

RECOMMENDING SERVICES IN A DIFFERNTIATED TRUST-BASED DECENTRALIZED USER MODELING SYSTEM

Trust and reputation mechanisms are often used in peer-to-peer networks, multi-agent systems and online communities for trust-based interactions among the users. Trust values are used to differentiate among members of the community as well as to recommend service providers. Although different users have different needs and expectations in different aspects of the service providers, traditional trust-based models do not use trust values on neighbors for judging different aspects of service providers. In this thesis, I use multi-faceted trust models for users connected in a network who are looking for suitable service providers according to their preferences. Each user has two sets of trust values: i) trust in different aspects of the quality of service providers, ii) trust in recommendations provided for these aspects. These trust models are used in a decentralized user modeling system where agents (representing users) have different preference weights in different criteria of service providers. My approach helps agents by recommending the best possible service provider for each agent according to its preferences. The approach is evaluated by conducting simulation on both small and large social networks. The results of the experiments illustrate that agents find better matches or more suitable service providers for themselves using my trust-based recommender system without the help of any central server. To the best of my knowledge this is the first system that uses multi-faceted trust values both in the qualities of service-providers and in other users’ ability to evaluate these qualities of service providers in a decentralized user modeling system

Reputation systems: Evaluating reputation among all good sellers

A reputation system assists people selecting whom to trust, encourages trustworthy action, and discourages participation of unskilled or dishonest. The “all good reputation” problem is common in current reputation systems, especially in e-commerce domain, making it difficult for buyers to choose credible sellers. Observing high growth of online data in Hindi language, in this paper, we propose a reputation system in this language. The functions of this system include (1) review mining for different criteria of online transactions, (2) calculation of reputation rating using Bayesian method, (3) calculation of reputation weight using typed dependency relation representation and Latent Dirichlet Allocation topic modeling technique for each criteria from user reviews, and (4) ranking sellers based on computed reputation score. Extensive simulations conducted on eBay dataset and TripAdvisor dataset show its

A Formal Framework for Modeling Trust and Reputation in Collective Adaptive Systems

Trust and reputation models for distributed, collaborative systems have been studied and applied in several domains, in order to stimulate cooperation while preventing selfish and malicious behaviors. Nonetheless, such models have received less attention in the process of specifying and analyzing formally the functionalities of the systems mentioned above. The objective of this paper is to define a process algebraic framework for the modeling of systems that use (i) trust and reputation to govern the interactions among nodes, and (ii) communication models characterized by a high level of adaptiveness and flexibility. Hence, we propose a formalism for verifying, through model checking techniques, the robustness of these systems with respect to the typical attacks conducted against webs of trust.Comment: In Proceedings FORECAST 2016, arXiv:1607.0200

WEB QUALITY, SATISFACTION, TRUST AND ITS EFFECTS ON GOVERNMENT WEBSITE LOYALTY

This research aims to analyze tourist loyalty on government websites that affected by service quality, reputation, consumer experience, satisfaction, and trust. This research is using data from 148 respondents who have been interacted with the website of visitingjogja.com. The data analysis technique is using two steps approach to Structural Equation Modeling (SEM). The research result shows that responsiveness as dimensional quality of website services has no influence toward consumer satisfaction, and system quality also has no influence on trust, so the model is modified. Modification is done by eliminating two insignificant paths. The result shows that reputation and experience has positive influence toward consumer satisfaction. Information quality and consumer satisfaction has positive influence toward trust, and trust has positive influence on website loyalty. Website loyalty model that proposed in thie research shows a fit result. Thus, this research result can improve generalization of research findings about website loyalty in the setting of government website user

Detection and Filtering of Collaborative Malicious Users in Reputation System using Quality Repository Approach

Online reputation system is gaining popularity as it helps a user to be sure about the quality of a product/service he wants to buy. Nonetheless online reputation system is not immune from attack. Dealing with malicious ratings in reputation systems has been recognized as an important but difficult task. This problem is challenging when the number of true user's ratings is relatively small and unfair ratings plays majority in rated values. In this paper, we have proposed a new method to find malicious users in online reputation systems using Quality Repository Approach (QRA). We mainly concentrated on anomaly detection in both rating values and the malicious users. QRA is very efficient to detect malicious user ratings and aggregate true ratings. The proposed reputation system has been evaluated through simulations and it is concluded that the QRA based system significantly reduces the impact of unfair ratings and improve trust on reputation score with lower false positive as compared to other method used for the purpose.Comment: 14 pages, 5 figures, 5 tables, submitted to ICACCI 2013, Mysore, indi

Semi-autonomous, context-aware, agent using behaviour modelling and reputation systems to authorize data operation in the Internet of Things

In this paper we address the issue of gathering the "informed consent" of an end user in the Internet of Things. We start by evaluating the legal importance and some of the problems linked with this notion of informed consent in the specific context of the Internet of Things. From this assessment we propose an approach based on a semi-autonomous, rule based agent that centralize all authorization decisions on the personal data of a user and that is able to take decision on his behalf. We complete this initial agent by integrating context-awareness, behavior modeling and community based reputation system in the algorithm of the agent. The resulting system is a "smart" application, the "privacy butler" that can handle data operations on behalf of the end-user while keeping the user in control. We finally discuss some of the potential problems and improvements of the system.Comment: This work is currently supported by the BUTLER Project co-financed under the 7th framework program of the European Commission. published in Internet of Things (WF-IoT), 2014 IEEE World Forum, 6-8 March 2014, Seoul, P411-416, DOI: 10.1109/WF-IoT.2014.6803201, INSPEC: 1425565