Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·Jun 9·5 min read
FuseFSS: Efficient Secure LLM Inference with Function Secret Sharing
TL;DR
Secure inference for large language models (LLMs) is hindered by inefficiencies in processing nonlinearities and helper operations. FuseFSS is a new compiler that optimizes these operations by streamlining the implementation of fixed-point nonlinearities and other tasks.
✦ Why It Matters
Engineers can implement FuseFSS to enhance the efficiency of secure LLM inference in their applications.
Key Takeaways
- Adopt FuseFSS for improved secure LLM inference performance.
- Utilize the single compilation pipeline for efficiency.
- Expect significant reductions in communication overhead.
- Leverage lower key sizes and generation times for security.
How It Works
FuseFSS compiles fixed-point operations into a streamlined process, generating two batched evaluations: one for packed comparisons that retrieves all predicate bits, and another for vector interval lookups that provides active coefficients and constants. This approach eliminates the need for separate protocols for each operation, significantly enhancing efficiency.