The extent to which an agent trusts another naturally depends on the outcomes of their interactions. Previous computational approaches have treated the outcomes in a domain-specific way. Specifically, these approaches focus on the mathematical aspect and assume that a positive or negative experience can be identified without showing how to ground the experiences in real-world interactions, such as emails and chats. We propose Güven, an approach that relates trust to the domain-independent notion of commitments. We consider commitments since commitment outcomes can be associated with experiences and a large body of works exist on commitments that include commitment representation and semantics. Also, recent research shows that commitments can be extracted from interactions, such as emails and chats. Thus, we posit Güven can provide an useful basis to infer trust between agents from their interactions. To evaluate Güven, we conducted empirical studies of two decision contexts. First, subjects read emails extracted from the Enron dataset (and augmented with some synthetic emails for completeness), and estimated trust between each pair of communicating agents. Second, the subjects played the Colored Trails game, estimating trust in their opponents. Güven incorporates a probabilistic model for trust based on commitment outcomes; we show how to train its parameters for each subject based on the subject’s assessments. The results are promising, though imperfect. Our main contribution is to launch a research program into computing trust based on a semantically well-founded account of interpersonal interactions.
Personas are a common tool used in Human Computer Interaction to represent the needs and expectations of a system’s stakeholders, but they are also grounded in large amounts of qualitative data. Our aim is to make use of this data to anticipate the differences between a user persona’s expectations of a system, and the expectations held by its developers. This paper introduces the idea of gulfs of expectation – the gap between the expectations held by a user about a system and its developers, and the expectations held by a developer about the system and its users. By evaluating these differences in expectation against a formal representation of a system, we demonstrate how differences between the anticipated user and developer mental models of the system can be verified. We illustrate this using a case study where persona characteristics were analysed to identify divergent behaviour and potential security breaches as a result of differing trust expectations.
We introduce the notion of digital-arbitration which enables resolving disputes between servers and users with the aid of arbitrators . Arbitrators are semi-trusted entities in the social network that facilitate communication or business transactions. The communicating parties, users and servers, agree before a communication transaction on a set of arbitrators they trust (reputation systems may support their choice). Then, the arbitrators receive a resource, e.g., a deposit, and a terms-of-use agreement between participants such that the resource of a participant is returned if and only if the participant acts according to the agreement. We demonstrate the usage of arbitrators in the scope of conditional anonymity. A user interacts anonymously with a server as long as the terms for anonymous communication are honored. If a server identifies a violation of the terms, it proves to the arbitrators that a violation took place and the arbitrators publish the identity of the user.
Reputation systems are an essential part of electronic marketplaces that provide a valuable method to identify honest sellers and punish malicious actors. Due to the continuous improvement of the computation models applied, advanced reputation systems have become non-transparent and incomprehensible to the end-user. As a consequence, users become skeptical and lose their trust toward the reputation system. In this work, we are taking a step to increase the transparency of reputation systems by means of providing interactive visual representations of seller reputation profiles. We thereto propose TRIVIA - a visual analytics tool to evaluate seller reputation. Besides enhancing transparency, our results show that through incorporating the visual-cognitive capabilities of a human analyst and the computing power of a machine in TRIVIA, malicious sellers can be reliably identified. In this way we provide a new perspective on how the problem of robustness could be addressed.
Wireless sensor network consists of a large number of resource constrained sensor nodes. These sensor nodes communicate over wireless medium to perform a variety of information processing functionality. Due to broadcast nature of wireless medium, security is one of the major concerns and overlapping sensing range of sensor nodes results in redundancy in sensing data. Moreover, a large amount of energy is consumed by the base station to process these redundant data. To conserve energy and enhance the lifetime of sensor nodes, redundancy is eliminated at intermediate nodes by performing data aggregation. Wireless sensor networks are generally deployed in untrusted and hostile environments which results in compromised nodes. Thus, security and reliability of the transmitted data get reduced. Compromised nodes can inject false data, drop all the data, selectively forward data to an attacker, copy legal nodes to join routing paths, and disrupt data transmission during the data aggregation operation. In this paper, a novel scheme for data aggregation based on trust and reputation model is presented to ensure security and reliability of aggregated data. It will help to select secure paths from sensor nodes to the base station; thereby the accuracy of aggregated data will be increased significantly. Simulations show that the proposed protocol LDAT has less energy consumption and more accuracy as compared to some existing protocols which are based on functional reputation.
This paper explores whether trust, developed in one context, transfers into another, distinct context and, if so, attempts to quantify the influence this prior trust exerts. Specifically, we investigate the effects of artificially stimulated prior trust as it transfers across disparate contexts and whether this prior trust can compensate for negative objective information. To study such incidents, we leveraged Berg’s investment game to stimulate varying degrees of trust between a human and a set of automated agents. We then observed how trust in these agents transferred to a new game by observing teammate selection in a modified, four-player extension of the well-known board game, Battleship. Following this initial experiment, we included new information regarding agent proficiency in the Battleship game during teammate selection to see how prior trust and new objective information interact. Deploying these experiments on Amazon’s Mechanical Turk platform further allowed us to study these phenomena across a broad range of participants. Our results demonstrate trust does transfer across disparate contexts and this inter-contextual trust transfer exerts a stronger influence over human behavior than objective performance data. That is, humans show a strong tendency to select teammates based on their prior experiences with each teammate, and proficiency information in the new context seems to matter only when the differences in prior trust between potential teammates are small.
In an online marketplace, buyers rely heavily on reviews posted by previous buyers (referred to as advisors). The advisor’s credibility determines the persuasiveness of reviews. Much work has addressed the evaluation of advisors’ credibility based on their static profile information, but little attention has been paid to the effect of the information about the history of advisors’ reviews. We conducted three sub-studies to evaluate how the advisors’ review balance (proportion of positive reviews) affects the buyer’s judgement of advisor’s credibility (e.g., trustworthiness, expertise). The result of study 1 shows that advisors with mixed positive and negative reviews are perceived to be more trustworthy, and those with extremely positive or negative review balance are perceived to be less trustworthy. Moreover, the perceived expertise of the advisor increases as the review balance turns from positive to negative; yet buyers perceive advisors with extremely negative review balance as low in expertise. Study 2 finds that buyers might be more inclined to misattribute low trustworthiness to low expertise when they are processing high number of reviews. Finally, study 3 explains the misattribution phenomenon and suggests that perceived expertise has close relationship with affective trust. Both theoretical and practical implications are discussed.
Often in open multiagent systems, agents interact with other agents to meet their own goals. Trust is, therefore, considered essential to make such interactions effective. However, trust is a complex, multifaceted concept and includes more than just evaluating others' honesty. Many trust evaluation models have been proposed and implemented in different areas; most of them focused on algorithms for trusters to model the trustworthiness of trustees in order to make effective decisions about which trustees to select. For this purpose, many trust evaluation models use third party information sources such as witnesses, but slight consideration is paid for locating such third party information sources. Unlike most trust models, the proposed model defines a scalable way to locate a set of witnesses, and combines a suspension technique with reinforcement learning to improve the model responses to dynamic changes in the system. Simulation results indicate that the proposed model benefits trusters while demanding less message overhead.
While trust management systems can be used in isolation in order to provide robustness to a given architecture, cooperation incentives can be used to complement and collaborate with trust management systems as users can benefit from them while using the system, thus encouraging user’s good behaviour. We have designed a fully decentralized trust management and cooperation incentives framework for user-centric network environments composed by three main components, the identity manager , the trust manager and the cooperation manager . In this article, we present how we integrate our trust management and cooperation incentives framework with a collaborative wireless access sharing service, being the aim of the article to evaluate its feasibility from a bootstrapping and survivability point of view. Our results obtained through simulation prove that the values for bootstrapping and data depletion times are well inside acceptable ranges, given that the total user base for the framework in the world is big enough while using friend-of-a-friend chains.
Identity management is a rather general concept that covers technologies, policies and procedures for recognising and authenticating entities in ICT environments. Current identity management solutions often have inadequate usability and scalability, or they provide inadequate authentication assurance. This article describes local user-centric identity management as an approach to providing scalable, secure and user friendly identity management. This approach is based on placing technology for identity management on the user side, instead of on the server side or in the cloud. This approach strengthens authentication assurance, improves usability, minimizes trust requirements, and has the advantage that trusted online interaction can be upheld even in the presence of malware infection in client platforms. More specifically, our approach is based on using an OffPAD (Offline Personal Authentication Device) as a trusted device to support the different forms of authentication that are necessary for trusted interactions. A prototype OffPAD has been implemented and tested in user experiments.
We propose a generic testbed for evaluating social trust models and we show how existing models can fit our tesbed. To showcase the flexibility of our design, we implemented a prototype and evaluated three trust algorithms, namely EigenTrust, PeerTrust and Appleseed, for their vulnerabilites to attacks and compliance to various trust properties. For example, we were able to exhibit discrepancies between EigenTrust and PeerTrust, as well as trade-offs between resistance to slandering attacks versus self-promotion.
Reputation systems have been extensively explored in various disciplines and application areas. A problem in this context is that the computation engines applied by most reputation systems available are designed from scratch and rarely consider well established concepts and achievements made by others. Thus, approved models and promising approaches may get lost in the shuffle. In this work, we aim to foster reuse in respect of trust and reputation systems by providing a hierarchical component taxonomy of computation engines which serves as a natural framework for the design of new reputation systems. In order to assist the design process we, furthermore, provide a component repository that contains design knowledge on both a conceptual and an implementation level. To evaluate our approach we conduct a descriptive scenario-based analysis which shows that it has an obvious utility from a practical point of view. Matching the identified components and the properties of trust introduced in literature, we finally show which properties of trust are widely covered by common models and which aspects have only rarely been considered so far.
The amount and variety of information currently available online is astounding. Information can be found covering any subject and is accessible from any part of the globe. While this is beneficial for countless purposes, whether they be in understanding situations or for making decisions, the sheer amount of information has led to significant problems in information overload . As humans, we are simply unable to consume, reason about, and act on such a vast quantity of information in a timely manner. This is especially true in cases where gathering a quick understanding or awareness of a situation is desirable, or even required. In this article, therefore, we aim to investigate an approach to helping address this problem, which builds on our previous research in the area of assessing and presenting the trustworthiness of online information. Specifically, this article examines the capability of tag (or word) clouds, coloured according to the trustworthiness of the contexts in which they appear, in supporting an individual’s understanding of a situation. The novelty of this work is in the application of such tag clouds to a new decision-making context, and engaging in a critical, user-based assessment of their use. To comment briefly on our findings, we note that there is potentially a significant value to be gained in the application of this technique, in providing a quick, helpful and accurate overview of a situation. This could be exploited by the public at large, but possibly even in more official investigative or crisis-management scenarios.
Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper, we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements.
The increasing use of Electronic Health Records has been mirrored by a similar rise in the number of security incidents where confidential information has inadvertently been disclosed to third parties. These problems have been compounded by an apparent inability to learn from previous violations; similar security incidents have been observed across Europe, North America and Asia. This has resulted in the loss of confidence and trust of the public towards the organisations’ ability to protect the patients’ private information. The Generic Security Template (G.S.T.) has been proposed to communicate security lessons learned from previous security incidents. This paper conducts a series of empirical studies to evaluate the usability of the G.S.T. The first study compares the G.S.T. with the conventional text-based security incident reports. The two methods were compared in term of the users’ ability to identify a number of lessons learned from investigations into previous incidents involving the disclosure of healthcare records. The study showed that the graphical approach resulted in higher accuracy in terms of number of correct answers generated by participants. However, subjective feedback raised further questions about the usability of the G.S.T. as the readers of security incident reports try to interpret the lessons that can increase the security of patient data. The second study further evaluates the usability of the G.S.T. using the Cognitive Dimensions and identifies some aspects that need to be improved.
Public displays may adapt intelligently to the social context, tailoring information on the screen, for example, to the profiles of spectators, their gender or based on their mutual proximity. However, such adaptation decisions should on the one hand match user preferences and on the other maintain the user’s trust in the system. A wrong decision can negatively influence the user’s acceptance of a system, cause frustration and, as a result, make users abandon the system. In this paper, we propose a trust-based mechanism for automatic decision-making, which is based on Bayesian Networks. We present the process of network construction, initialization with empirical data, and validation. The validation demonstrates that the mechanism generates accurate decisions on adaptation which match user preferences and support user trust.
A principal carrying out a delegation may not be certain about the state of its delegation graph as it may have been perturbed by an attacker. This perturbation may come about from the attacker concealing the existence of selected delegation certificates and/or injecting new delegation certificates. As a consequence of this delegation subterfuge the principal may violate its own policy that guides delegation actions. This paper considers the verification of the absence of subterfuge in systems that accept and issue delegation certificates. It is argued that this absence of subterfuge is not a safety property and a non-interference style security-property based interpretation is proposed.
In this paper, we offer an algorithm for intelligent decision making about travel path planning in mobile vehicular ad-hoc networks (VANETs), for scenarios where agents representing vehicles exchange reports about traffic. One challenge that arises is how best to model the trustworthiness of those traffic reports. To this end, we outline an algorithm for effectively soliciting, receiving and analyzing the trustworthiness of these reports, to drive a vehicle’s decision about the path to follow. Distinct from earlier work, we clarify the need for specifying the conditions under which reports are exchanged and for processing non-binary reports, culminating in a proposed algorithm to achieve that processing, as part of the trust modeling and path planning. To validate our approach we then offer a detailed evaluation framework that achieves large scale simulation of traffic, travel and reporting of information, confirming the value of our proposed approach by demonstrating the average speed of vehicles which follow our algorithm (compared to ones that do not). This experimental framework is promoted as a significant contribution towards the goal of evaluating trust algorithms for intelligent decision making in traffic scenarios.
Information is the currency of the digital age – it is constantly communicated, exchanged and bartered, most commonly to support human understanding and decision-making. While the Internet and Web 2.0 have been pivotal in streamlining many of the information creation and dissemination processes, they have significantly complicated matters for users as well. Most notably, the substantial increase in the amount of content available online has introduced an information overload problem, while also exposing content with largely unknown levels of quality, leaving many users with the difficult question of, what information to trust? In this article we approach this problem from two perspectives, both aimed at supporting human decision-making using online information. First, we focus on the task of measuring the extent to which individuals should trust a piece of openly-sourced information (e.g., from Twitter, Facebook or a blog); this considers a range of factors and metrics in information provenance, quality and infrastructure integrity, and the person’s own preferences and opinion. Having calculated a measure of trustworthiness for an information item, we then consider how this rating and the related content could be communicated to users in a cognitively-enhanced manner, so as to build confidence in the information only where and when appropriate. This work concentrates on a range of potential visualisation techniques for trust, with special focus on radar graphs, and draws inspiration from the fields of Human-Computer Interaction (HCI), System Usability and Risk Communication. The novelty of our contribution stems from the comprehensive approach taken to address this very topical problem, ensuring that the trustworthiness of openly-sourced information is adequately measured and effectively communicated to users, thus enabling them to make informed decisions.
