OUTPUT 001: [CHINCHILLA] MODEL WEIGHT ENCRYPTION PROTOCOL
BREAKTHROUGH: SCALABLE AI ENCRYPTION
We've solved the fundamental challenge of AI privacy: how to run encrypted inference at scale without prohibitive computational overhead.
THE PROBLEM
Current AI privacy solutions don't scale:
- Homomorphic Encryption: 100-1000x slower
- Secure Enclaves: Hardware lock-in, <312MB limits
- Policy-Based Privacy: Soft trust, policy changes
OUR INNOVATION
Unique model weight encryption enabling:
- End-to-end encrypted AI inference
- <5% performance overhead
- Compatible with standard cloud infrastructure
- Proven on LLaMA models up to 70B parameters
TECHNICAL APPROACH
Input Text
The quick brown fox
↓
Permutation
P
↓
Token IDs
17 7 9 3
↓
Permuted Embedding Layer
9 3 17 7
↓
Model
↓
Permuted lm.head
↓
Inverse Permutation
P-1
↓
Output
jumps over the lazy dog
VALIDATION
- ✓ VLLM integration
- ✓ Compatible with OpenAI API standards
- ✓ Ready for private enterprise deployments on dedicated hardware
COMPETITIVE ADVANTAGE
Our model weight encryption protocol delivers enterprise-grade AI privacy without the crushing performance penalties of Fully Homomorphic Encryption (100-1000x slower) or the hardware constraints of Secure Enclaves (<312MB limits), achieving near-native performance on standard cloud infrastructure while maintaining complete cryptographic privacy.