Privacy-Preserving Machine Learning: Split Learning and Privacy Attacks

Tech Talk from ODSC West 2021

Legitimate privacy concerns often prevent us from making use of distributed data stored in protected silos. How can we enable practitioners to perform advanced analytics on this sensitive (siloed) data whilst safeguarding and not compromising the original data points?

This tech talk explores one of the potential solutions, split learning, a new method for training a modular deep neural network where each module lives in a data silo while upholding quantifiable standards for privacy and security. We will also dive into the privacy implications of training and releasing the model, including common privacy attacks and general use cases for federated analytics.