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Trust semantic learning and monitoring

Measure on-going trust between interacting agents

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.

Run by ChiTek-i AS

Logo NLnet: abstract logo of four people seen from above Logo NGI Assure: letterlogo shaped like a tag

This project was funded through the NGI Assure Fund, a fund established by NLnet with financial support from the European Commission's Next Generation Internet programme, under the aegis of DG Communications Networks, Content and Technology under grant agreement No 957073.

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Please check out NLnet's theme funds, such as NGI Assure and NGI Zero Entrust.

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