Autonomic trust prediction for pervasive systems

In recent years, various trust management models based on the human notion of trust have been proposed to support trust-aware decision making in pervasive systems. However, the degree of subjectivity embedded in human trust often clashes with the requirements imposed by the target scenario: on one hand, pervasive computing calls for autonomic and light-weight systems that impose minimum burden on the user of the device (and on the device itself); on the other hand, computational models of human trust seem to demand a large amount of user input and physical resources. The result is often a computational trust model that does not 'compute': either the degree of subjectivity it offers is limited, or its complexity compromises its usability. In this paper, we present an accurate and efficient trust prediction model that is based on a basic Kalman filter. We discuss simulation results to demonstrate that the predictor is capable of capturing the natural disposition to trust of the user of the device, while being autonomic and light-weight.