The Promise & Pitfalls of Public Data in Private ML

Machine learning models are frequently trained on large-scale datasets, which may contain sensitive or personal data. Worryingly, without special care, these models are prone to revealing information about datapoints in their training set, leading to violations of individual privacy. To protect against such privacy risks, we can train models with differential privacy (DP), a rigorous notion of individual data privacy. While training models with DP has previously been observed to result in unacceptable losses in utility, I will discuss recent advances which incorporate public data into the training pipeline, allowing models to guarantee both privacy and utility. I will also discuss potential pitfalls of this approach, and directions forward for the community. 

_________________________________________________________________

About the series:

The Vector Distinguished Talk series is a formal gathering of academic and industrial data scientists across the Greater Toronto Area (GTA) to discuss advanced topics in machine learning and its goal is to build a stronger machine learning community in Toronto.

The talks will be given by international and local faculty and industry professionals. The seminar series is intended for university faculty and graduate students in machine learning across computer science, ECE, statistics, mathematics, linguistics, and medicine, as well as PhD-level data scientists doing interesting applied research in the GTA. The Toronto machine learning community will be stronger when we know each other and know what problems people are working on. At the end of each talk there will also be an opportunity to have in-person meetings with the speaker.

Vector Distinguished Lecture Series is currently being held remotely. We look forward to welcoming everyone in-person at Vector’s new office space soon. Our talks will continue to be streamed online for those not able to come in-person.

Register here!

This event is open to the public with emphasis on graduate students in machine learning, computer science, ECE, statistics, mathematics, linguistics, medicine, as well as PhD-level data scientists in the GTA.