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With the growing development and deployment of large language models (LLMs) in both industrial and academic fields, their security and safety concerns have become increasingly critical. However, recent studies indicate that LLMs face numerous vulnerabilities, including data poisoning, prompt injections, and unauthorized data exposure, which conventional methods have struggled to address fully. In parallel, blockchain technology, known for its data immutability and decentralized structure, offers a promising foundation for safeguarding LLMs. In this survey, we aim to comprehensively assess how to leverage blockchain technology to enhance LLMs' security and safety. Besides, we propose a new taxonomy of blockchain for large language models (BC4LLMs) to systematically categorize related works in this emerging field. Our analysis includes novel frameworks and definitions to delineate security and safety in the context of BC4LLMs, highlighting potential research directions and challenges at this intersection.Through this study, we aim to stimulate targeted advancements in blockchain-integrated LLM security. 

Zero-Knowledge proofs are a cryptographic technique to reveal knowledge of information without revealing the information it- self, thus enabling systems optimally to mix privacy and trans- parency, and, where needed, regulatability. Application domains include health and other enterprise data, financial systems such as central-bank digital currencies, and performance enhancement in blockchain systems. The challenge of zero-knowledge proofs is that, although they are computationally easy to verify, they are computationally hard to produce. This paper examines the scala- bility limits of leading zero-knowledge algorithms and addresses the use of parallel architectures to meet performance demands of applications.