No abstract

We propose a trust management framework based on measurement theory to infer indirect trust in online social communities using trust’s transitivity property. Inspired by the similarities between human trust and measurement, we propose a new trust metric, composed of impression and confidence, which captures both trust level and its certainty. Furthermore, based on error propagation theory, we propose a method to compute indirect confidence according to different trust transitivity and aggregation operators. We perform experiments on two real data sets, Epinions.com and Twitter, to validate our framework. Also, we show that inferring indirect trust can connect more pairs of users.

Internet of Things (IoT) can connect a large number of things (or agents) through communication networks for various types of applications. Like in many other applications, it is very important for all the agents in IoT systems to collaborate with each other following predefined protocols. In this paper, we proposed a general trust management framework aiming to help agents to evaluate their partners' trustworthiness. We run a simulation for a food nutrition analysis example. It shows that by using trust, the analysis error can be reduced. Also, we illustrate two possible types of attacks, and show how to use different trust factors or environments together to alleviate the damage.