An efficient and versatile approach to trust and reputation using hierarchical Bayesian modelling

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

Multiagent systems (MASs) have a wide range of industrial applications due to agents’ advantages. However, because of the agents’ dynamic behaviors, it is a challenge to ensure the quality of service they present. In this paper, to address this problem, we propose an adaptive agent trust estimation model where agents may decide to go from genuine to malicious or the other way around. In the proposed trust model, both direct trust and indirect reputation are used. However, the indirect reputation derived from the direct experience of third-party agents must have reasonable confidence to be useful. The proposed model introduces a near-perfect measure that utilizes consistency, credibility, and certainty to capture confidence. Moreover, agents are incentivized to contribute correct information (to be honest) through a credit mechanism in the proposed model. Simulation experiments are conducted to evaluate the proposed model’s performance against some of the previous trust models reported in the literature.

Improving trust and reputation assessment with dynamic behaviour

Trust between agents in multi-agent systems (MASs) is critical to encourage high levels of cooperation. Existing methods to assess trust and reputation use direct and indirect past experiences about an agent to estimate their future performance; however, these will not always be representative if agents change their behaviour over time.

Real-world distributed networks such as online market places, P2P networks, pervasive computing and the Smart Grid can be viewed as MAS. Dynamic agent behaviour in such MAS can arise from seasonal changes, cheaters, supply chain faults, network traffic and many other reasons. However, existing trust and reputation models use limited techniques, such as forgetting factors and sliding windows, to account for dynamic behaviour.

In this paper, we propose Reacting and Predicting in Trust and Reputation (RaPTaR), a method to extend existing trust and reputation models to give agents the ability to monitor the output of interactions with a group of agents over time to identify any likely changes in behaviour and adapt accordingly. Additionally, RaPTaR can provide an a priori estimate of trust when there is little or no interaction data (either because an agent is new or because a detected behaviour change suggests recent past experiences are no longer representative). Our results show that RaPTaR has improved performance compared to existing trust and reputation methods when dynamic behaviour causes the ranking of the best agents to interact with to change.

Internet-of-Things and Information Fusion: Trust Perspective Survey

The advent of Internet-of-Things (IoT) is creating an ecosystem of smart applications and services enabled by a multitude of sensors. The real value of these IoT smart applications comes from analyzing the information provided by these sensors. Information fusion improves information completeness/quality and, hence, enhances estimation about the state of things. Lack of trust and therefore, malicious activities renders the information fusion process and hence, IoT smart applications unreliable. Behavior-related issues associated with the data sources, such as trustworthiness, honesty, and accuracy, must be addressed before fully utilizing these smart applications. In this article, we argue that behavior trust modeling is indispensable to the success of information fusion and, hence, to smart applications. Unfortunately, the area is still in its infancy and needs further research to enhance information fusion. The aim of this article is to raise the awareness and the need of behavior trust modelling and its effect on information fusion. Moreover, this survey describes IoT architectures for modelling trust as well as classification of current IoT trust models. Finally, we discuss future directions towards trustworthy reliable fusion techniques.

Reputation assessment: a review and unifying abstraction

Trust and reputation allow agents to make informed decisions about potential interactions. Trust in an agent is derived from direct experience with that agent, while reputation is determined by the experiences reported by other witness agents with potentially differing viewpoints. These experiences are typically aggregated in a trust and reputation model, of which there are several types that focus on different aspects. Such aspects include handling subjective perspectives of witnesses, dishonesty, or assessing the reputation of new agents. In this paper, we distil reputation systems into their fundamental aspects, discussing first how trust and reputation information is represented and second how it is disseminated among agents. Based on these discussions, a unifying abstraction is presented for trust and reputation systems, which is demonstrated by instantiating it with a broad range of reputation systems found in the literature. The abstraction is then instantiated to combine the range of capabilities of existing reputation systems in the Machine Learning Reputation System, which is evaluated using a marketplace simulation.