Background and Related Work

The landscape of data transfer has evolved significantly, fueled by the exponential growth of digital data and the global shift towards digitization in various sectors. Traditional methods of data sharing, ranging from email attachments to cloud storage solutions, have been foundational yet fraught with security vulnerabilities. These conventional mechanisms often fall short in protecting data against unauthorized access, interception, and cyber threats, raising substantial concerns over privacy and data integrity.

Recent advancements in cryptography, blockchain technology, and secure multi-party computation have paved the way for more secure data transfer methods. However, these solutions can be complex to implement, may not scale efficiently, or could introduce significant latency in data access and sharing processes. Moreover, compliance with evolving data protection regulations, such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States, has added layers of complexity to data sharing practices, necessitating solutions that offer both security and ease of compliance.

In this context, Large Language Models (LLMs) have emerged as a groundbreaking technology with the potential to revolutionize data transfer. LLMs, powered by advanced algorithms and vast datasets, can understand, generate, and manipulate human language in a way that mimics human cognitive processes. Their application in secure data transfer represents a novel approach, combining the power of artificial intelligence with cutting-edge security protocols to facilitate safe, efficient, and compliant data sharing.