BumbleBee: Secure Two-party Inference Framework for Large Transformers
Wen-jie Lu
Network and Distributed System Security (NDSS) Symposium 2025 · Day 1 · Privacy & Cryptography 1
This article delves into "BumbleBee," a novel secure two-party inference framework designed for large transformer models, presented by Wen-jie Lu from Zjanu at the NDSS Symposium. The talk addresses a critical challenge in the era of pervasive AI services: how to leverage powerful machine learning models without compromising the privacy of sensitive input data. As individuals and organizations increasingly rely on cloud-based AI, the necessity to transmit confidential information to remote servers raises significant concerns about potential data exposure, misuse, or mismanagement.
AI review
Solid applied-crypto research on a real and underserved problem — private inference for large transformers without trusted hardware. The contributions are genuine (IR-level graph rewriting, numerically stable approximations, 100x communication reduction) but the work sits comfortably within an established research lineage rather than breaking new ground. Right venue, right lane, competent execution.