PC–BC–LLMs: A Deep Fusion Architecture for Trusted AI Applications

In this paper, we propose a novel integrated framework for trustworthy AI systems, which deeply fuses privacy computing (PC), blockchain (BC), and large language models (LLMs) into a cohesive unit (PC-BC-LLMs). LLMs have emerged as significant drivers of digital economic growth and industrial transformation. However, their widespread adoption has raised significant data privacy and security concerns that conventional centralized security approaches have struggled to address. Despite the potential of PC and BC to offer solutions, there is still a lack of an integrated framework that systematically combines these technologies with LLMs to address practical issues of privacy, trust, and compliance. Our proposed PC-BC-LLMs deep fusion framework views these technologies as a cohesive unit designed to establish an end-to-end trustworthy AI service chain. In this framework, we analyze the synergistic roles of each component: privacy computing secures data confidentiality and invisibility during computation; blockchain serves as a decentralized trust anchor, ensuring the integrity and traceability of data and computational processes; and LLMs serve as the intelligent core, leveraging their robust analytical capabilities while safeguarding privacy. Future research should prioritize investigating the bidirectional enabling mechanism of LLMs and PC/BC. In this paper, we demonstrate the practical application of this framework through two industrial case studies: Intelligent Manufacturing Quality Control and Enterprise Data Compliance Audits. Furthermore, we address the primary challenges and future research directions concerning technical standardization and the balance between performance and safety within the framework. Our study offers a viable approach for establishing a reliable and sustainable AI data collaboration technology system and presents a novel perspective for future research in this domain.