Privacy Preserving AI in the Enterprise
Differential privacy is making headlines thanks to the pioneering work of companies like Apple and Google, and it is now being used by companies of all sizes to provide data privacy guarantees. It is no secret that machine learning models can memorize (overfit) training data and that through carefully crafted adversarial inputs machine learning models can be subverted by an attacker. Combine these facts with a model that aggregates data from a multitude of customers and you have an AI-driven disaster waiting to happen. In this talk we will cover a defensive measure called “differential privacy” that is a potential solution to such threats. In this talk Yevgeniy will explain the core concepts of differential privacy and share a behind the scenes look at companies are successfully implementing differential privacy in their products.
Yevgeniy Vahlis is the head of applied machine learning at Borealis AI. Prior to joining Borealis AI Yevgeniy lead an applied research team at Georgian Partners, a late stage venture capital fund, and worked at a number of tech companies including Amazon and Nymi. Yevgeniy started his career at AT&T Labs in New York as a research scientist after completing his PhD in computer science at UofT and spending a year at Columbia university as a postdoc.