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.