Chain-of-Trust: A Progressive Trust Evaluation Framework Enabled by Generative AI

TL;DR

This paper introduces a progressive trust evaluation framework called chain-of-trust that uses generative AI to efficiently assess device trustworthiness in complex, dynamic collaborative systems by evaluating trust in multiple stages.

Contribution

It presents a novel multi-stage trust evaluation framework that leverages generative AI for analyzing partial data, reducing complexity and improving accuracy in dynamic environments.

Findings

High accuracy in trust evaluation demonstrated

Efficient use of device data at each stage

Effective handling of network dynamics and data misalignment

Abstract

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.