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Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
FromThe TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
ratings:
Length:
34 minutes
Released:
Feb 7, 2020
Format:
Podcast episode
Description
Today we’re joined by Ryan Rogers, Senior Software Engineer at LinkedIn. We caught up with Ryan at NeurIPS, where he presented the paper “Practical Differentially Private Top-k Selection with Pay-what-you-get Composition” as a spotlight talk. In our conversation, we discuss how LinkedIn allows its data scientists to access aggregate user data for exploratory analytics while maintaining its users’ privacy with differential privacy, and the major components of the paper. We also talk through one of the big innovations in the paper, which is discovering the connection between a common algorithm for implementing differential privacy, the exponential mechanism, and Gumbel noise, which is commonly used in machine learning. The complete show notes for this episode can be found at twimlai.com/talk/346.
Released:
Feb 7, 2020
Format:
Podcast episode
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