Trust Model for Optimized Cloud Services

Cloud computing with its inherent advantages draws attention for business critical applications, but concurrently expects high level of trust in cloud service providers. Reputation-based trust is emerging as a good choice to model trust of cloud service providers based on available evidence. Many existing reputation based systems either ignore or give less importance to uncertainty linked with the evidence. In this paper, we propose an uncertainty model and define our approach to compute opinion for cloud service providers. Using subjective logic operators along with the computed opinion values, we propose mechanisms to calculate the reputation of cloud service providers. We evaluate and compare our proposed model with existing reputation models

Engineering Trust - Reciprocity in the Production of Reputation Information

Reciprocal feedback distorts the production and content of reputation information, hampering trust and trade efficiency. Data from eBay and other sources combined with laboratory data provide a robust picture of how reciprocity can be guided by changes in the way feedback information flows through the system, leading to more accurate reputation information, more trust and more efficient trade.market design; reputation; trust; reciprocity; eBay

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

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 systems. 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

Engineering Trust - Reciprocity in the Production of Reputation Information

Reciprocity in feedback giving distorts the production and content of reputation information in a market, hampering trust and trade efficiency. Guided by feedback patterns observed on eBay and other platforms we run laboratory experiments to investigate how reciprocity can be managed by changes in the way feedback information flows through the system, leading to more accurate reputation information, more trust and more efficient trade. We discuss the implications for theory building and for managing the redesign of market trust systems.market design, reputation, trust, reciprocity, eBay

Flow-based reputation with uncertainty: Evidence-Based Subjective Logic

The concept of reputation is widely used as a measure of trustworthiness based on ratings from members in a community. The adoption of reputation systems, however, relies on their ability to capture the actual trustworthiness of a target. Several reputation models for aggregating trust information have been proposed in the literature. The choice of model has an impact on the reliability of the aggregated trust information as well as on the procedure used to compute reputations. Two prominent models are flow-based reputation (e.g., EigenTrust, PageRank) and Subjective Logic based reputation. Flow-based models provide an automated method to aggregate trust information, but they are not able to express the level of uncertainty in the information. In contrast, Subjective Logic extends probabilistic models with an explicit notion of uncertainty, but the calculation of reputation depends on the structure of the trust network and often requires information to be discarded. These are severe drawbacks. In this work, we observe that the `opinion discounting' operation in Subjective Logic has a number of basic problems. We resolve these problems by providing a new discounting operator that describes the flow of evidence from one party to another. The adoption of our discounting rule results in a consistent Subjective Logic algebra that is entirely based on the handling of evidence. We show that the new algebra enables the construction of an automated reputation assessment procedure for arbitrary trust networks, where the calculation no longer depends on the structure of the network, and does not need to throw away any information. Thus, we obtain the best of both worlds: flow-based reputation and consistent handling of uncertainties

Acceptance of feedbacks in reputation systems: the role of online social interactions

In an online environment, the aim of reputation systems is to let parties rate each other and to help consumers in deciding whether to transact with a given party. In current reputation systems for e-commerce, users have to trust unreliable information sources and anonymous people. As a result, users are not only hesitant to trust online seller but also to reputation systems. Therefore, there is a need to improve current reputation systems by allowing users to make buying decision based on reliable source of information. This paper proposes a new approach of sharing knowledge and experience in reputation systems by utilizing social interactions. This study examines the potentials of integrating social relations information in reputation systems by proposing a model of acceptance of feedbacks in reputation systems

How Social Reputation Networks Interact with Competition in Anonymous Online Trading: An Experimental Study

Many Internet markets rely on ‘feedback systems’, essentially social networks of reputation, to facilitate trust and trustworthiness in anonymous transactions. Market competition creates incentives that arguably may enhance or curb the effectiveness of these systems. We investigate how different forms of market competition and social reputation networks interact in a series of laboratory online markets, where sellers face a moral hazard. We find that competition in strangers networks (where market encounters are one-shot) most frequently enhances trust and trustworthiness, and always increases total gains-from-trade. One reason is that information about reputation trumps pricing in the sense that traders usually do not conduct business with someone having a bad reputation not even for a substantial price discount. We also find that a reliable reputation network can largely reduce the advantage of partners networks (where a buyer and a seller can maintain repeated exchange with each other) in promoting trust and trustworthiness if the market is sufficiently competitive. We conclude that, overall, competitive online markets have more effective social reputation networks.reputation systems, e-commerce, internet markets, trust

Reconsidering online reputation systems

Social and socioeconomic interactions and transactions often require trust. In digital spaces, the main approach to facilitating trust has effectively been to try to reduce or even remove the need for it through the implementation of reputation systems. These generate metrics based on digital data such as ratings and reviews submitted by users, interaction histories, and so on, that are intended to label individuals as more or less reliable or trustworthy in a particular interaction context. We suggest that conventional approaches to the design of such systems are rooted in a capitalist, competitive paradigm, relying on methodological individualism, and that the reputation technologies themselves thus embody and enact this paradigm in whatever space they operate in. We question whether the politics, ethics and philosophy that contribute to this paradigm align with those of some of the contexts in which reputation systems are now being used, and suggest that alternative approaches to the establishment of trust and reputation in digital spaces need to be considered for alternative contexts