Trust in Network Society

This work is carried out in collaboration with NLnet and begins with the founding ideas in these works:

Trust semantic learning and monitoring is part of a wide ranging effort to understand trust in network socio-technical systems. The expected outcome of this part is a methodology and proof of concept code library for qualifying and quantifying trust between agents in a network. In IT, trust is often treated as a binary "crypto token", based on some validation test, and developers naively speak of zero trust systems without understanding the depth of what trust really is. But, trust is a deeply social phenomenon, which changes in real time based on social and technical interactions. By applying learning algorithms and data analytics to streamed interactions, this project attempts to qualify and quantify a measure of trust as a way of making realtime risk estimates.

Project outcomes

  1. Literature summary (in progress) (pdf)