SentinelLMs: Encrypted Input Adaptation and Fine-tuning of Language Models for Private and Secure Inference. (arXiv:2312.17342v1 [cs.CR])

This paper addresses the privacy and security concerns associated with deep
neural language models, which serve as crucial components in various modern
AI-based applications. These models are often used after being pre-trained and
fine-tuned for specific tasks, with deployment on servers accessed through the
internet. However, this introduces two fundamental risks: (a) the transmission
of user inputs to the server via the network gives rise to interception
vulnerabilities, and (b) privacy concerns emerge as organizations that deploy
such models store …

addresses applications components deployment encrypted fine-tuning input language language models privacy privacy and security private security security concerns servers