Equivariant Encryption enables large models to perform inference directly on encrypted data without compromising privacy or performance.

• Addresses critical privacy concerns in distributed AI deployments
• Maintains confidentiality of sensitive user data during inference
• Offers practical alternative to costly homomorphic encryption and MPC techniques
• Enables secure AI applications in privacy-sensitive domains like healthcare and finance

This breakthrough approach significantly reduces the security-efficiency tradeoff, making private AI more feasible for real-world applications while protecting against data exposure threats.

Encrypted Large Model Inference: The Equivariant Encryption Paradigm