Encryption Protocols for Secure AI Systems: A Practical Guide

Short summary

AI systems processing sensitive data face a critical gap: standard encryption protects data in transit and at rest but not during computation. This guide covers four essential layers every production deployment needs—homomorphic encryption, zero-knowledge proofs, trusted execution environments, and post-quantum cryptography—with performance benchmarks and selective application patterns for each. HE carries 10x-100x computational overhead, ZKPs add 5x-50x proof generation cost, so workload-specific deployment is essential.

• •Homomorphic encryption enables computation directly on ciphertext, protecting data during inference without plaintext exposure
• •Zero-knowledge proofs verify computation integrity (model provenance, inference audits, gradient correctness) without revealing weights or inputs
• •Post-quantum cryptography and selective federated learning protection are critical for systems handling medical records, financial data, or proprietary training sets

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