Privacy-Preserving AI: Implementation Guide for Six Methods

Privacy-Preserving AI Gets a Practitioner Playbook Nick Lothian's implementation guide distills four years of hands-on experience into a structured comparison of six privacy-preserving techniques, spanning local model deployment, TEEs, differential privacy, SMPC, federated learning, and homomorphic encryption. The guide emphasizes composition strategies, recognizing that real-world systems rarely rely on a single method. ML engineers building compliant pipelines under GDPR or HIPAA should prioritize this as a decision framework. Unlike academic surveys, the focus is operational tradeoffs, not theoretical guarantees.