Reputation assessment: a review and unifying abstraction

Abstract

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.

Trust assessment in 32 KiB of RAM

“…There has been much work on developing trust models to solve these problems beyond resource-constrained systems [32,38]. In vehicle and cellular networks the task offloading problem (which we focus on in this paper) is referred to as Multi-access Edge Computing (MEC) (previously Mobile Edge Computing) [25].…”

“…An issue in trust-based selection is that when the system is starting or a new entrant joins, there is little opportunity for historical data to have been gathered and used to build a trust model. Therefore, in order for facilitate better initial decisions, stereotypes can be provided as a starting point to bootstrap trust models [32].…”

“…However, a selection problem then arises: which edge node should an IoT node choose to submit a task to? This is typically addressed in the literature by evidence-based behavioural trust [32,38], where the incidence of how well an edge node has correctly executed tasks in the past is recorded and used as a predictor of how likely that edge node is to correctly execute future tasks. Trust is typically evaluated at the application level and sufficient storage is needed to record information about each edge node of interest.…”

Improving trust and reputation assessment with dynamic behaviour

“…Stereotyping techniques improve assessments of newcomers or agents for whom there is no past experience (Burnett et al, 2010;Sensoy et al, 2016). More recently, trust assessment has been viewed as a machine-learning problem with a trust model including a learning component and a predictive component (Lu & Lu, 2017;Taylor et al, 2018).…”

“…Methods to assess trust and reputation have been developed for domains including P2P networks (Kamvar et al, 2003;Tahta et al, 2015;Xiong & Liu, 2004), pervasive computing (D'Angelo et al, 2017;Klusch & Gerber, 2002), the internet of things (Chen et al, 2016) and many more. While existing trust models are often tailored to particular contexts or require domaindependent information, recent trust models increasingly use machine learning to generalize trust models (Liu et al, 2014;Taylor et al, 2018). However, two main challenges remain, namely, how to assign a trust value in the face of no evidence and how to capture and detect dynamic behaviours (Traverso et al, 2017).…”