Differentially Private Multi-party Data Release for Linear Regression

Explore the intricacies of differentially private multi-party data release for linear regression in this 57-minute Google TechTalk presented by Ruihan Wu. Delve into the Differential Privacy for ML Series as the speaker discusses advanced techniques for protecting privacy in collaborative data analysis scenarios. Gain insights into the challenges and solutions surrounding secure data sharing among multiple parties while maintaining statistical utility for linear regression tasks. Learn about cutting-edge approaches to balance privacy preservation with accurate model training in multi-party settings.