Having your cake and eating it: An implementation guide for privacy with AI
Everyone wants privacy, but the best models require you to give up control of your data. What options are there for keeping data private but while still embracing the promise of AI?
In this talk we’ll take a practical, experience based look at options ranging from private models, trusted execution environments, differential privacy, multi-part computation, federated learning and homomorphic encryption (and yes I’ll explain what these are!)
I’ll explain what each is, when they are useful and give my personal experience with running some of these in production (the ones that made it that far!) over the past 4 years.
Nick Lothian
Nick Lothian is a AI/ML engineer and technical leader with deep experience in AI, applied machine learning, forecasting, NLP, and privacy preserving technologies. Nick has led or contributed to projects that won the National AI Centre Sprint (2024) and the Red Dot Design Award (2022), and has built AI systems used across industry, government, and national security.
Nick’s background spans CTO, Head of ML, and Head of Product roles. Nick is a co-author on papers in PNAS and ACM, and a co-inventor on an NLP geolocation patent.
Nick also help grow the Australian AI ecosystem as Adelaide Lead for AI.Build Club and Co-Founder of the Artificial Intelligence Collaborative Network.