Just came across this paper (arXiv:2502.01013) that could be huge for private local model deployment.
The researchers achieved 99.999% accuracy on encrypted neural network inference with literally zero additional latency. Not “minimal” overhead - actually zero.
The key insight: instead of using homomorphic encryption (10,000x slowdown), they train networks to use “equivariant functions” that commute with encryption operations. So you can compute directly on AES or ChaCha20 encrypted data.
What this means for local LLMs:
-
Your prompts could remain encrypted in memory
-
Model weights could be encrypted at rest
-
No performance penalty for privacy
The catch: you need to retrain models with their specific architecture constraints. Can’t just plug this into existing models.…
Just came across this paper (arXiv:2502.01013) that could be huge for private local model deployment.
The researchers achieved 99.999% accuracy on encrypted neural network inference with literally zero additional latency. Not “minimal” overhead - actually zero.
The key insight: instead of using homomorphic encryption (10,000x slowdown), they train networks to use “equivariant functions” that commute with encryption operations. So you can compute directly on AES or ChaCha20 encrypted data.
What this means for local LLMs:
-
Your prompts could remain encrypted in memory
-
Model weights could be encrypted at rest
-
No performance penalty for privacy
The catch: you need to retrain models with their specific architecture constraints. Can’t just plug this into existing models.
Paper: https://arxiv.org/abs/2502.01013
Also made a technical breakdown analyzing the limitations they gloss over: https://youtu.be/PXKO5nkVLI4
Anyone see potential applications for local assistant privacy? The embedding layer limitations seem like the biggest bottleneck for LLM applications.