The advancement of large language models (LLMs) has significantly enhanced the ability to
effectively tackle various downstream NLP tasks and unify these tasks into generative …

Large transformer-based models have realized state-of-the-art performance on lots of real-
world tasks such as natural language processing and computer vision. However, with the …

Secure transformer inference has emerged as a prominent research topic following the
proliferation of ChatGPT. Existing solutions are typically interactive, involving substantial …

With the growing use of large language models hosted on cloud platforms to offer inference
services, privacy concerns are escalating, especially concerning sensitive data like …

With the growing use of Transformer models hosted on cloud platforms to offer inference
services, privacy concerns are escalating, especially concerning sensitive data like …

Many inference services based on large language models (LLMs) pose a privacy concern,
either revealing user prompts to the service or the proprietary weights to the user. Secure …

Abstract Machine Learning (ML) is rapidly advancing, enabling various applications that
improve people's work and daily lives. However, this technical progress brings privacy …

We present Rhombus, a new secure matrix-vector multiplication (MVM) protocol in the semi-
honest two-party setting, which is able to be seamlessly integrated into existing privacy …

We propose an efficient non-interactive privacy-preserving Transformer inference
architecture called Powerformer. Since softmax is a non-algebraic operation, previous …

Recent advancements in transformers have revolutionized machine learning, forming the
core of Large language models (LLMs). However, integrating these systems into everyday …