Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference

Abstract

The applications of Generative Artificial Intelligence (GenAI) and their intersections with data-driven fields, such as healthcare, finance, transportation, and information security, have led to significant improvements in service efficiency and low latency. However, this synergy raises serious concerns regarding the security of large language models (LLMs) and their potential impact on the privacy of companies and users' data. Many technology companies that incorporate LLMs in their services wi

Categories

@misc{abdennebi2026fully,
  title = {{Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference}},
  author = {Anes Abdennebi and Nadjia Kara and Laaziz Lahlou},
  year = {2026},
  month = apr,
  eprint = {2604.12168},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2604.12168}
}