Natalie Dullerud | CaPC – Confidential and Private Collaborative Learning

The Foresight Institute Podcast

Natalie Dullerud is a first-year PhD student at Stanford University and recently received her Master’s from University of Toronto. She previously graduated with a Bachelor’s degree in mathematics from University of Southern California, with minors in computer science and chemistry. Natalie’s research largely focuses on machine learning through differential privacy, algorithmic fairness, and applications to clinical and biological settings.
Machine learning benefits from large training datasets, which may not always be possible to collect by any single entity, especially when using privacy-sensitive data. In many contexts, such as healthcare and finance, separate parties may wish to collaborate and learn from each other’s data but are prevented from doing so due to privacy regulations. This talk will describe Confidential and Private Collaborative (CaPC) Learning, a recently published framework that allows entities to collaborate via privacy-preserving mechanisms in order to improve their own local models and maintain data privacy, from both a cryptographic and differentially private definition. The talk will encompass current definitions, concerns and techniques in the private distributed learning setting, then turn to the technical details and advantages to CaPC in practical settings.
Session summary: Natalie Dullerud | CaPC - Confidential and Private Collaborative Learning - Foresight Institute

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Allison Duettmann is the president and CEO of Foresight Institute. She directs the Intelligent Cooperation, Molecular Machines, Biotech & Health Extension, Neurotech, and Space Programs, Fellowships, Prizes, and Tech Trees, and shares this work with the public. She founded Existentialhope.com, co-edited Superintelligence: Coordination & Strategy, co-authored Gaming the Future, and co-initiated The Longevity Prize.

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