In the first episode of our Differential Privacy series, I’m joined by Aaron Roth, associate professor of computer science and information science at the University of Pennsylvania.
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Aaron is first and foremost a theoretician, and our conversation starts with him helping us understand the context and theory behind differential privacy, a research area he was fortunate to begin pursuing at its inception. We explore the application of differential privacy to machine learning systems, including the costs and challenges of doing so. Aaron discusses as well quite a few examples of differential privacy in action, including work being done at Google, Apple and the US Census Bureau, along with some of the major research directions currently being explored in the field.
Thanks to our Sponsor!
Thanks to Georgian Partners for their continued support of the podcast and for sponsoring this series. Georgian Partners is a venture capital firm that invests in growth stage business software companies in the US and Canada. Post investment, Georgian works closely with portfolio companies to accelerate adoption of key technologies including machine learning and differential privacy. To help their portfolio companies provide privacy guarantees to their customers, Georgian recently launched its first software product, Epsilon, which is a differentially private machine learning solution. You’ll learn more about Epsilon in my interview with Georgian’s Chang Liu later this week, but if you find this field interesting, I’d encourage you to visit the differential privacy resource center they’ve set up at https://gptrs.vc/twimlai
Mentioned in the Interview
- Cynthia Dwork
- Calibrating Noise to Sensitivity in Private Data Analysis
- Dr. Latanya Sweeney
- “Robust De-anonymization of Large Sparse Datasets – Privacy Lives.”
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“More On That Later” by Lee Rosevere licensed under CC By 4.0