Practical Differential Privacy at LinkedIn with Ryan Rogers - #346
Published:Feb 7, 2020 19:39
•1 min read
•Practical AI
Analysis
This article discusses a podcast episode featuring Ryan Rogers, a Senior Software Engineer at LinkedIn. The core topic revolves around the implementation of differential privacy at LinkedIn to protect user data while enabling data scientists to perform exploratory analytics. The conversation focuses on Rogers' paper, "Practical Differentially Private Top-k Selection with Pay-what-you-get Composition." The discussion highlights the use of the exponential mechanism, a common algorithm in differential privacy, and its relationship to Gumbel noise. The article suggests a practical application of differential privacy in a real-world scenario, emphasizing the balance between data utility and user privacy.
Key Takeaways
- •LinkedIn uses differential privacy to protect user data while allowing data analysis.
- •The discussion centers around the "Practical Differentially Private Top-k Selection with Pay-what-you-get Composition" paper.
- •The exponential mechanism and Gumbel noise are key components of the discussed differential privacy implementation.
Reference
“The article doesn't contain a direct quote, but it discusses the content of a podcast episode.”