Chain-of-Trust: A Progressive Trust Evaluation Framework Enabled by Generative AI
This addresses trust evaluation for task completion in collaborative systems, but it appears incremental as it builds on existing methods like in-context learning for a specific domain.
The paper tackles the challenge of comprehensive trust assessment in collaborative systems with distributed resources by proposing a progressive trust evaluation framework called chain-of-Trust, which uses generative AI to analyze misaligned device attribute data in stages, achieving high accuracy in experiments.
In collaborative systems with complex tasks relying on distributed resources, trust evaluation of potential collaborators has emerged as an effective mechanism for task completion. However, due to the network dynamics and varying information gathering latencies, it is extremely challenging to observe and collect all trust attributes of a collaborating device concurrently for a comprehensive trust assessment. In this paper, a novel progressive trust evaluation framework, namely chain-of-trust, is proposed to make better use of misaligned device attribute data. This framework, designed for effective task completion, divides the trust evaluation process into multiple chained stages based on task decomposition. At each stage, based on the task completion process, the framework only gathers the latest device attribute data relevant to that stage, leading to reduced trust evaluation complexity and overhead. By leveraging advanced in-context learning, few-shot learning, and reasoning capabilities, generative AI is then employed to analyze and interpret the collected data to produce correct evaluation results quickly. Only devices deemed trustworthy at this stage proceed to the next round of trust evaluation. The framework ultimately determines devices that remain trustworthy across all stages. Experimental results demonstrate that the proposed framework achieves high accuracy in trust evaluation.