Differentially Private Simple Linear Regression

Explore a 37-minute conference talk by Harvard University's Salil Vadhan on differentially private simple linear regression. Delve into the intersection of privacy-preserving techniques and statistical analysis, focusing on how differential privacy can be applied to linear regression models. Gain insights into the challenges and solutions for maintaining individual privacy while extracting meaningful insights from data. Learn about the latest advancements in this field as part of the "Workshop on Differential Privacy and Statistical Data Analysis" hosted by the Fields Institute. Understand the implications of these techniques for data science, machine learning, and privacy-conscious statistical analysis.

Differentially Private Simple Linear Regression