Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings

Machine learning has the potential to improve health care through its ability to extract information from data. Unfortunately, machine learning is susceptible to privacy attacks which leak information about the data it was trained on. This can have dire consequences in health care where protecting patient privacy is of the utmost importance. Differential privacy has been proposed as the leading technique to defend against privacy attacks and has had successful use by the US Census, Google, and Apple. This talk will present the challenges of using differentially private machine learning in health care and how future solutions might address them.

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This is an online event. It will be live streamed on the Centre for Ethics YouTube Channel on Wednesday, October 28. Channel subscribers will receive a notification at the start of the live stream. (For other events in the series, and to subscribe, visit YouTube.com/c/CentreforEthics.)

Vinith Suriyakumar
Department of Computer Science
University of Toronto

Wed, Oct 28, 2020
04:00 PM - 05:00 PM
Centre for Ethics, University of Toronto
200 Larkin