arXiv:2606.16461v1 Announce Type: new Abstract: Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers. Split inference partially mitigates this by keeping tokens on the client and sending only hidden representations, but these representations can still be recovered via nearest-neighbor search against the public embedding table. We propose an orthogonal obfuscation procedure in which the client multiplies embeddings by a secret orthogonal matrix before transmission. To enable correct inference under arbitrary rotations, we intro
The increasing reliance on cloud-based LLM inference for sensitive data, coupled with growing privacy concerns, is driving innovation in methods to secure these operations.
This development offers a potential pathway to enhance data privacy for organizations utilizing third-party LLM services, reducing the risk of sensitive information leakage from embedding representations.
The ability to perform LLM inference with stronger privacy guarantees through orthogonal obfuscation and equivariant transformers changes the compute-security trade-off for sensitive applications.
- · Organizations with sensitive data
- · Privacy-focused AI service providers
- · LLM security researchers
- · Eavesdroppers
- · Providers with poor data security postures
Companies will be more comfortable using cloud LLMs for sensitive internal data.
Increased adoption of privacy-preserving LLM inference techniques could become a standard requirement for enterprise AI.
The development of robust and provably secure LLM inference methods could accelerate the deployment of AI in highly regulated sectors without on-premise compute requirements.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG