Trust and Reputation Management

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BIONETS WP 4 SECURITY UNIVERSITY OF TRENTO. D4.1 Trust and Reputation Management System Definition

BIONETS WP 4 SECURITY UNIVERSITY OF TRENTO. D4.1 Trust and Reputation Management System Definition

The unique BIONETS architecture (see Section 6) and the features of the BIONETS systems presented in Sections 6.1 and 6.2 impose the definition of specific functions to define a suitable reputation management system. Moreover, the presence of heterogeneous entities and the communication constrains, locality and U-Node/T-Node interaction, impose the definition of new reputation types for these elements which are different from classical peer-to-peer systems. In addition, the BIONETS system is composed by services that must be considered an important separate entity and must be monitored for performance and behaviour. Table 2 highlights new concepts introduced in the BIONETS system and the consideration made to design a reputation management system suitable for BIONETS. These new concepts make the use of rep- utation management systems already in literature impractical and impose the implementation of new func- tionalities. In this context we define proper communcation model for gathering information from nodes and disseminate trust values in the system. The next sections consider the general properties (presented in Sec- tion 5) a reputation management system should implement and analyze them in the context of BIONETS by presenting solutions concepts and definitions which are proper of BIONETS.
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Trust and Reputation Management: a Probabilistic Approach

Trust and Reputation Management: a Probabilistic Approach

increasing their own reputation or decreasing the reputation of others. eBay is a popular com- mercial example of reputations systems that employ the summation method. In eBay buyers and sellers rate each others based on their transactions as positive, neutral, or negative. The overall reputation of a seller is mostly represented by a feedback score that is computed by summing all the positive ratings minus all the negative ones. Eigentrust, a reputation management framework in P2P networks, also adopted the summation method for feedback aggregation [89]. In Eigen- trust, the truster weighs the trust feedbacks received from other peers by their corresponding trust scores. The aggregated trust score assigned to a trustee is then the sum of the product of the trust feedbacks and the trust scores of the feedbacks senders. PeerTrust, another P2P trust system, also employs the summation method with various trust metrics, taking into account the credibility of the feedbacks senders [90]. Alternatively, [91, 92] introduced multiple operators to handle different scenarios of trust propagation in a network of interacting agents. For instance, the “Concatenation” operator is used when computing the trust of an agent A in agent C based on the trust of agent B in C discounted by the trust of A in B.
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ITERATIVE ALGORITHMS FOR TRUST AND REPUTATION MANAGEMENT AND RECOMMENDER SYSTEMS

ITERATIVE ALGORITHMS FOR TRUST AND REPUTATION MANAGEMENT AND RECOMMENDER SYSTEMS

with some well-known and commonly used reputation management techniques (e.g., Averaging Scheme, Bayesian Approach and Cluster Filtering) indicates the superior- ity of the proposed scheme both in terms of robustness against attacks and efficiency. The introduction of the belief propagation and iterative message passing methods onto trust and reputation management has opened up several research directions. Thus, next, the first application of the belief propagation algorithm in the design of recommender systems (BPRS) is proposed. In BPRS, recommendations (predicted ratings) for each active user are iteratively computed by probabilistic message passing between variable and factor nodes in a factor graph. It is shown that as opposed to the previous recommender algorithms, BPRS does not require solving the recommen- dation problem for all users if it wishes to update the recommendations for only a single active user using the most recent data (ratings). Further, BPRS computes the recommendations for each user with linear complexity, without requiring a training period while it remains comparable to the state of art methods such as Correlation- based neighborhood model (CorNgbr) and Singular Value Decomposition (SVD) in terms of rating and precision accuracy.
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TRUST IN A NETWORKED WORLD: EXPERIMENTAL GAMES FOR THE DESIGN OF REPUTATION MANAGEMENT SYSTEMS

TRUST IN A NETWORKED WORLD: EXPERIMENTAL GAMES FOR THE DESIGN OF REPUTATION MANAGEMENT SYSTEMS

In e-business we observe successful trust enhancement by reputation management systems. The most popular of those is ebays’s Feedback Forum. At ebay, anonymous individuals spread over the globe may buy and sell almost everything from Pez dispensers to Ferraris or castles. With nearly 50 million registered users and 170 million transactions (for $ 9.3 billion worth of goods) in 2001, it is the largest of the informal online markets [13]. These numbers are impressive given the risks involved in trading on such a market. Typically there is no opportunity for a buyer to inspect the item for which he has paid before delivery, and if he isn’t satisfied with the quality, it may be impossible for him to track down the seller. Even worse, the buyer has no guarantee that the item will be delivered at all. The seller, on the other hand, if he chooses to deliver before receiving the payment faces similar risks with respect to the buyer. To put it differently, each of the parties involved in a trade might be tempted to cheat. Ebay has a fraud protection program that covers losses for up to $200. However, beyond that, if users do not want to make use of costly escrow services that ebay also offers, they must bring a large portion of trust when they engage in transactions on this informal online market.
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Weighted Graph Approach for Trust Reputation Management

Weighted Graph Approach for Trust Reputation Management

VII. CONCLUSION AND FUTURE WORK In this study, trust reputation management through weighted signed graph is reemphasized the age old golden rule: “The Client is supreme over the Agent”. This method can be applied for finding redundancy in web pages, for removing spurious ranking of products in retail industry and in avoiding unwanted short messages in the mobile phones from the mobile networks.

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A Trust Based Reputation Management System

A Trust Based Reputation Management System

First, and foremost, I would like to thank my supervisor, Prof. Vinny Cahill, who provided an excellent map to guide me, both in terms of research and in terms of life, as well as so many words of encouragement, faith, kindness, and patience when I could not see what was ‘round the bend’. Thanks also to the members of various research groups for providing opportunities to collaborate and learn. In particular, special thanks to Christian Jensen for bringing me into the SECURE consortium. I am also so very appreciative of the members of the DSG research group, especially Stefan Weber, Anthony Harrington, Peter Barron, Yong Chen, Barbara Hughes, Ray Cunningham, Cormac Driver, Gregor Gaertner, John Keeney, René Meier, Andronikos Nedos, Jean-Marc Seigneur, and Kulpreet Singh. Much appreciation is also due Alan Mullally and Denise Leahy, of the Dept. of Information Systems, for creating an environment in which I could teach and learn. Thanks also to Yong Wang of the Beijing Institute of Technology. Furthermore, to Audun Jøsang of the Queensland University of Technology, thank you for sharing your vast experience in the field of reputation management. Thank you to the many friends who provided advice, support, caring, and willingness to listen to ramblings about trust-based decision-making models. Above all, this one’s for the girls: Treasa Ní Mhíocaín, Judith Murphy, Keelin Murphy, Elizabeth Daly, and Ivana Dusparic. Each of you is a guardian angel, and even when we lost the map entirely, somehow you managed to make sure I got to the end of the journey in one piece and with so many incredible memories. Also, Geraldine McNamara and Piers Gardiner, thank you for continually opening your hearts and home to me. Gratitude and appreciation especially to Niall Rea, who stayed fast during the lowest points, pacing with me, step by step, always shining a light around corners and bringing tranquillity.
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Trust and Reputation Management in Decentralized Systems

Trust and Reputation Management in Decentralized Systems

P2P networks are networks in which all peers cooperate with each other to perform a critical function in a decentralized manner. All peers are both consumers and providers of resources. Compared with a centralized system, a P2P system provides an easy way to aggregate large amounts of resources residing on the Internet or in ad-hoc networks with a low cost of system maintenance. The decentralization, however, also causes some problems. Since peers are heterogeneous, some peers might be benevolent in providing services. Some might be buggy and cannot provide the services they advertise. Some might be malicious by providing bad services. There is no centralized entity to serve as an authority to supervise peers’ behaviors and punish peers that behave badly. Malicious peers may have an incentive to harm other peers if they can get a benefit, because they can get away with it. Some traditional security techniques, such as service providers requiring access authorization, or consumers requiring server authentication, are used as protection from known malicious peers. However, they cannot protect from peers providing variable-quality service, or peers that are unknown. Mechanisms for trust and reputation can be used to help peers distinguish good partners from bad ones. This chapter describes a trust and reputation mechanism that allows peers to discover partners who meet their individual requirements, through individual experience and experiences shared by other peers with similar preferences.
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How can the corporate sector concepts of 'reputation' and 'trust' be used by local government? A study to establish a model of reputation management for local government

How can the corporate sector concepts of 'reputation' and 'trust' be used by local government? A study to establish a model of reputation management for local government

Discussion of reputation and its affect on the success of a corporation has been considerable since the 1990s (published in the Harvard Business Review and quoted in Bergen, 1999; 1999; Dowling, 2001; Eberl & Schwaiger, 2005; Charles J Fombrun, 1996, 2002; Nakra, 2000; Ou & Abratt, 2006; Shapiro, 2001). Yankelovich Partners and Fortune Magazine found that companies engaged in reputation management had a price/earnings ratio 12.5% higher than those who were not. For the average Fortune 500 company, this translated into an increase in market value of $5bn (quoted in Bergen, 1999). This is supported by studies by the University of Texas (Fombrun and Foss, 2001), Vegrin and Qoronfleh (published in the Harvard Business Review and quoted in Nakra, 2000), Pennsylvania State University (Richardson and Bolesh, 2002), Oxford University (Carroll, 1999; Eberl & Schwaiger, 2005), and Heal (quoted in Deni Greene, 2001).
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Trust and Reputation Building in E-Commerce

Trust and Reputation Building in E-Commerce

Trust may play a critical role in the development of the Internet as a marketplace. For example, in the emerging world of business-to-business collaboration, successful collaborations require trust in their partners to behave ethically. A company that shares internal data such as sales reports, production schedules, product designs and logistical details with a supply-chain partner must trust the partner with that information. Other examples where trust plays a role are informal online markets where individuals may buy and sell a wide variety of goods and services. In these markets, single, isolated trades often take place between anonymous counterparts. There may be no opportunity for inspection of the item to be traded. Thus, each of the trading parties might be tempted to cheat. As a buyer of Beanie Babies at eBay (http://www.ebay.com), for example, I face some risk that the seller has not accurately described the condition of his Beanie Babies, will not pack them properly, or will not deliver them in a timely fashion. To manage this kind of risk several approaches have been proposed (see, for example, Kollock 1999, and Malaga 2001.) For example, third party escrow services could be used. They have the disadvantage, though, that they are time-consuming and costly (service charges). It is sometimes advised to reduce some of the risk related with online trading by frequent communication with the trading partner and by insisting on the revelation of enough information to make the trading partner identifiable. However, there seems to be little hope of actually tracking down a trading partner, given the opportunities to disguise identities due to, for example, free e-mail services. As a more powerful approach, many of the online market sites have developed reputation management systems that allow the trading parties to submit a rating of the counter party’s performance in a specific transaction, which will be made available to all visitors of the site. A positive rating of my trading partner is likely to increase my trust in the performance of the counter party.
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ONLINE REPUTATION MANAGEMENT

ONLINE REPUTATION MANAGEMENT

Your online reputation is your image on the Internet. Online reputation management (ORM) is about improving or restoring your name or your brand’s good standing. This is by countering, weakening or eliminating the negative material found in the Internet – defeating it with more positive material to improving your credibility and customers’ trust in you.

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AN Effective Handling of Credibility and Reputation Based Trust Management for Cloud Services for Cloud Data Security

AN Effective Handling of Credibility and Reputation Based Trust Management for Cloud Services for Cloud Data Security

India. Abstract_ In cloud computing growth, the management of trust element is most challenging issue. Cloud computing has produce high challenges in security and privacy by the changing of environments. Trust is one of the most concerned obstacles for the adoption and growth of cloud computing. Although several solutions have been proposed recently in managing trust feedbacks in cloud environments, how to determine the credibility of trust feedbacks is mostly neglected. In this project the system proposed a Cloud Armor, a reputation-based trust management framework that provides a set of functionalities to deliver Trust as a Service (TaaS). “Trust as a Service” (TaaS) framework to improve ways on trust management in cloud environments. The approaches have been validated by the prototype system and experimental results. Keywords: Cloud computing, Trust, Obstacles, Reputation, Feedbacks.
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Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust on Intention for Online Transactions

Online Trust: The Influence of Perceived Company’s Reputation on Consumers’ Trust and the Effects of Trust on Intention for Online Transactions

According to D. Maditinos and K. Theodoridis [24] there is low internet and technology infusion, as well as lim- ited online market in Greece. According to N. K. Mal- hotra and J. D. McCort [25], important cultural differ- ences between different countries extend to the e-com- merce context. L. Chai and P. Pavlou [19] elaborated the case of cultural differences and other factors influence the electronic commerce adoption. They have endorsed that there is an important cultural dimension which is “uncertainty avoidance” and refers to how much people feel threatened by ambiguity. It is supported that Greece’s score is the highest for any country measured [19]. Be- sides, L. Chai and P. Pavlou state that “this distinct cul- tural dimension is suggested to moderate consumers’ in- tentions to adopt e-commerce”. There is a moderating ef- fect of uncertainty in the intention to purchase on-line. Since in Greece people feel importantly threatened by ambiguity, online trust is of high significance in the Greek context. “Countries with high uncertainty avoid- ance, such as Greece, dislike uncertain situations and prefer to act only under known conditions” [19]. A com- mon mistake is to assume that all customer behaviour is similar. Managers of online shopping companies should modify their approaches, depending on the culture they are targeting [19]. When managers attempt to penetrate in the Greek market, they should focus on creating and fostering a safe online transactions image [19]. Accord- ing to D. Maditinos and K. Theodoridis [24], the security perception is positively related to e-commerce customer satisfaction which is related to the intention of a con- sumer to repurchase through internet. Therefore it should be a priority to create a strong company’s local identity and presence in the local country [19].
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Engineering Trust - Reciprocity in the Production of Reputation Information

Engineering Trust - Reciprocity in the Production of Reputation Information

Our study also has implications for managing the redesign of market trust systems. First, a major challenge in solving marketplace trust problems has to do with possible adverse side-effects or disruptions in path dependencies in migrating to a new system. For example, a redesign of a trust system need respect the fact that reciprocity has positive as well as negative consequences for the feedback system. The giving of feedback is largely a public good, and our data suggest that reciprocity is important for getting mutually satisfactory trades recorded. It is therefore desirable that, in mitigating retaliatory feedback, we strive for a targeted approach rather than one that attempts to remove all forms of reciprocity. Also, by nature, reputation mechanisms are embedded in repeated games, connecting past with future behavior. It was important to the present redesign to maintain certain aspects of the old system, such as the 3-point (conventional) scoring, so that the information collected prior to the change in the system would still be useful in evaluating traders after the changeover, without causing undue confusion.
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the company behind the brand: in reputation we trust

the company behind the brand: in reputation we trust

Companies are using a wide variety of tools available to build and reinforce their reputations. When executives were asked how important various com- munications channels are to their companies’ reputation, most agreed with consumers — word of mouth is most influential (94%). Also highly rated are news sources (91%) and leadership communications (91%). Leaders are recognized as important reputation content-providers. Online tools are also widely seen as influential reputation-makers, although social networks are the exception. As discussed earlier, until companies fully embrace social network- ing as a unique engagement channel, its potential will remain untapped. Nearly all the channels that drive reputation are rated high — over 80% — in this study. Awards and rankings, advertising, business conferences are all rated by more than eight out of 10 executives as critical to influ- encing reputation. Clearly, executives recognize that company reputation must be communicated across many channels, not one or two. Content fusion and strategic distribution are key strategies for the next generation of reputation-builders.
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Designing Online Marketplaces: Trust and Reputation Mechanisms

Designing Online Marketplaces: Trust and Reputation Mechanisms

creating online reputation systems, several design challenges have been documented in the context of reviews. First, on platforms with reciprocal reviewing (i.e., where buyers and sellers review each other), users can have strategic incentives to manipulate reviews (Bolton et al. 2013, Fradkin et al. 2015). Second, reviews can suffer from selection bias (Hu et al. 2009, Masterov et al. 2015), as the people leaving reviews may differ from those who do not. Third, reviews may be distorted by promotional content in which businesses attempt to leave reviews for themselves (Mayzlin, Dover, and Chevalier 2014; Luca and Zervas 2015). Moreover, even if all reviews represent a user’s true experience, some users may be more informative than others (Dai et al. 2015). Section 3 provides an overview of these issues, as well as potential design solutions for these challenges.
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Seller Reputation and Trust in Pre-Trade Communication

Seller Reputation and Trust in Pre-Trade Communication

We characterize the unique equilibrium in which high ability sellers always announce the quality of their items truthfully, in a repeated game model of experienced good markets with adverse selection on a seller's propensity to supply good quality items. In this equilibrium a seller's value function strictly increases in reputation and a seller's type is revealed within finite time. The analysis highlights a new reputation mechanism based on an endogenous complementarity the market places between a seller's honesty in pre-trade communication (trust) and his/her ability to deliver good quality (reputation). As maintaining honesty is less costly for high ability sellers who anticipate less “bad news” to disclose, they can signal their ability by communicating in a more trustworthy manner. Applying this model, we examine the extent to which consumer feedback systems foster trust in online markets, including the possibility that sellers may change identities or exit.
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A Probabilistic Trust Model for Handling Inaccurate Reputation Sources

A Probabilistic Trust Model for Handling Inaccurate Reputation Sources

In the short term, we will be carrying out empirical analysis on TRAVOS, and evaluating it against similar approaches. As it stands, TRAVOS assumes that the behaviour of agents does not change over time, but in many cases this is an unsafe assumption. In particular we believe that agents may well change their behaviour over time, and that some will have time-based behavioural strategies. Future work will therefore include the removal of this assumption and the use of functions that allow an agent to take into account the fact that very old experiences may not be relevant in predicting the behaviour of an individual. In addition we will extend the model to represent a continuous outcome space, instead of the current binary outcome space. Further extensions to TRAVOS will include using the rich social metadata that exists within a VO environment in the calculation of a trust value. Thus, as described in Section 1, VOs are social structures, and we can draw out social data such as roles and relationships that exist both between VOs and VO members. The incorporation of such data into the trust metric should allow for more accurate trust assessments to be formed.
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TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources

Abstract In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested, and when trusted to perform an action for another, may betray that trust by not performing the action as required. In addition, due to the size of such systems, agents will often interact with other agents with which they have little or no past experience. There is therefore a need to develop a model of trust and reputation that will ensure good interactions among software agents in large scale open sys- tems. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents, and when there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate. Keywords Trust · Reputation · Probabilistic trust
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Certified Reputation   How an Agent Can Trust a Stranger

Certified Reputation How an Agent Can Trust a Stranger

on witness reports, the problem of disinformation has come to the fore in several recent works on trust. Regret [8] mod- els a witness’ credibility based on the difference between that witness’ opinion and an agent’s past experience. This differs to our approach in that it depends on the availabil- ity of an agent’s past experience. Moreover, this approach cannot deal with the situations where the target agent’s be- haviour changes since it does not take the new behaviour into account in its witness credibility assessment. Thus, it can falsely punish honest witnesses (who report the target agent’s new behaviour). In Whitby et al.’s system [11], the “true” rating of an agent is defined by the majority’s opin- ions. In particular, they model the performance of an agent as a beta probability density function (PDF) which is aggre- gated from all witness ratings received. Then a witness is considered unreliable and filtered out when the reputation derived from its ratings is judged to be too different from the majority’s (by comparing the reputation value with the PDF). Due to the dependency on PDFs of witness reports, if these reports are scarce and/or too diverse it is not able to recognise lying witnesses. Moreover, it is possible that a wit- ness can lie in a small proportion of their reports without being detected. In addition, isolated honest opinions (i.e. different than that of the majority) can be falsely punished. To rectify this, TRAVOS uses an approach that is similar to ours [10]. However, as it uses beta PDFs for representing trust derived from binary outcomes (i.e. 0 for ‘failed’, 1 for ‘success’), it is not suitable for our CR model because we require a more fine-grained and continuous range for trust values. In our earlier work [5], we presented a preliminary model of CR. However, this model could not cope with col- lusion and did not take the variance of referees situations (see Section 3.3) into account.
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An Investigation into Trust & Reputation for Agent Based Virtual Organisations

An Investigation into Trust & Reputation for Agent Based Virtual Organisations

In Section 1.3, we stated that the focus of our research is service-provision trust, specif- ically in contexts in which human assessment of trust is not practical, and discussed the importance of this type of trust in large open distributed systems, such as the Grid. Against this background, the overall aim of our research is to develop a computational model for assessing the trustworthiness of an agent, to provide a particular service. In this section, we detail a set of requirements that are relevant to this goal, which we refer to subsequently when assessing our own work and other computational trust models. Essentially, assessing the trust in an agent can be viewed as an on-line learning prob- lem, in which the future behaviour of that agent must be predicted given the available evidence. Depending on the situation, there may be a variety of predictive information sources that can be used to perform this task. These sources may have varying degrees of predictive power, and may or may not be available in a given situation. For instance, if a truster has previously interacted with a trustee before, then the truster can use past observations of the trustee’s behaviour to estimate the outcome of a future interaction. However, if a truster has not previously interacted with a trustee, it must rely on other information, such as the behaviour of other similar trustees it has interacted with. In light of this, the challenge for trust assessment is to identify sources of evidence, fuse this information in a way that respects the relative predictive value of each source, and find mechanisms to cope when any particular source is unavailable. Furthermore, this must be done such that the opportunity for a trustee to outwit the learning process, by behaving in a certain way, is minimised. In the following subsections we expand on this by identifying three key sets of requirements: general requirements, that must be addressed by any solution to this problem; additional requirements, imposed on solutions aimed at large-scale distributed systems; and reputation requirements, which apply when information about a trustee is gathered via a third party.
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