Homomorphic Encryption Moves From Theory to Private AI Inference Pipelines

Homomorphic encryption, first proven feasible by Craig Gentry in 2009, allows computations to be performed on encrypted data without ever decrypting it. The technology has matured from a theoretical concept into real-world applications, with banks, hospitals, and tech giants like Google now deploying it in production systems. Unlike standard encryption schemes, homomorphic encryption preserves algebraic structure so that operations on ciphertexts mirror operations on the underlying plaintext, keeping inputs and outputs private from the server. The CKKS scheme has emerged as a practical approach for machine learning workloads by enabling approximate arithmetic on encrypted vectors. Despite growing adoption, significant performance and complexity costs mean engineers must carefully evaluate whether a private inference pipeline justifies the overhead.