FuseFSS: Efficient Secure LLM Inference with Function Secret Sharing
Two-server secure inference allows a client to query a hosted large language model (LLM) without revealing prompts or embeddings. Recent GPU systems based on function secret sharing (FSS) make linear layers efficient, but fixed-point nonlinearities and helper operations remain a bottleneck because each operator is typically implemented as a bespoke protocol with its own comparisons, wrap-around corrections, and preprocessing material. We present FuseFSS, a compiler that replaces per-operator protocol design with a single compilation pipeline. For each scalar fixed-point operator, a compact specification lists its interval partition, low-degree arithmetic pieces, and required predicate bit...
| Authors | Yuhan Ma, Yong Li, Stefan Schmid |
|---|---|
| Primary category | cs.CR |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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