International Association
for Cryptologic Research

Generative AI, a significant technological disruptor in recent years, has impacted domains like augmented reality, coding assistance, and text generation. However, use of these models requires users to trust the model owners with their sensitive data given as input to the model. Fully Homomorphic Encryption (FHE) offers a promising solution, and many earlier works have investigated the use this technology for machine learning as a service (MLaaS) applications. Still, these efforts do not cater to generative models that operate probabilistically, allowing for diverse and creative outputs. In this work, we introduce three novel probabilistic selection algorithms for autoregressive generative AI: multiplication-scaled cumulative sum, heuristic cumulative sum, and the random-multiplication argmax. Each of these approaches presents distinctive challenges in optimizing the trade-off between precision and timing performance, a balance intricately tied to the specific characteristics of the data under consideration. Our results show that the random multiplication argmax-based method is more scalable than the cumulative sum methods and can accurately mimic the plaintext selection curve.