TLDR Tech / InfoSec
Having your cake and eating it: An implementation guide for privacy with AI — Nick Lothian at AI Engineer Melbourne 2026
This piece explores the privacy-versus-capability trade-off across six methods: private local models, trusted execution environments, differential privacy, secure multi-party computation, federated learning, and homomorphic encryption. It draws on Nick Lothian's four years of hands-on experience. Private models and Trusted Execution Environments (TEEs) are shown as the most practical defaults, while homomorphic encryption is still too slow for full pipelines and should be used mainly for specific cryptographic tasks. AI system architects should view privacy as a composition problem: combining federated training with local inference, layering differential privacy with trusted execution environments, and applying heavy cryptographic primitives only where their benefits justify the costs.
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