arXiv:2606.09551v1 Announce Type: cross Abstract: 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. F
The increasing deployment of large language models for sensitive applications necessitates robust privacy-preserving inference methods, addressing a key barrier to widespread adoption.
This development enables secure LLM inference without revealing proprietary prompts or embeddings, which is critical for privacy-conscious industries and sovereign AI initiatives.
The bottleneck in secure LLM inference, previously due to complex implementation of nonlinear operations, is mitigated by a compiler-based approach, streamlining development and improving efficiency.
- · Privacy-focused AI companies
- · Healthcare sector
- · Financial services
- · Government agencies
- · Less efficient secure inference methods
- · Organizations relying on insecure LLM deployments
Increased adoption of privacy-preserving LLMs across sensitive data domains.
Acceleration in the development and availability of secure AI-powered applications, leading to new market opportunities.
Enhanced trust and regulatory acceptance for AI solutions, potentially shaping future data privacy legislation globally.
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Read at arXiv cs.AI