Homomorphic Encryption for AI Explainer
Homomorphic encryption is a form of encryption that allows selected mathematical operations to be performed directly on ciphertext. After decryption, the result matches what the permitted computation would have produced on the original data.
In AI, a client can encrypt an input, send it to a computing service, and receive an encrypted prediction. The service evaluates a compatible model without learning the client’s plaintext input or the final decrypted answer.
Schemes differ in which operations they support and how much accumulated noise they tolerate. Neural networks may need polynomial approximations, quantization, batching, or architecture changes to fit the encrypted computation.
The protection is powerful but not complete. Keys, client devices, model outputs, metadata, and surrounding software still require security. Encrypted inference is also slower and more resource-intensive than ordinary computation, so deployments must measure the tradeoff.
Homomorphic encryption allows certain AI computations to run on encrypted data so the computing service does not need to see the underlying values.
Key terms include plaintext, ciphertext, key, encrypted inference, homomorphic operation, noise budget, polynomial approximation, batching and decryption.
The client encrypts the input before it reaches the computing service.
Compatible operations transform ciphertext into an encrypted prediction for the client to decrypt.
Supported operations, ciphertext growth, latency, and memory shape the model design.
Endpoints and metadata remain outside the mathematical guarantee.
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