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Improving the Privacy-Utility Tradeoff in Differentially Private Machine Learning with Public Data

Offered By: Google TechTalks via YouTube

Tags

Differential Privacy Courses Machine Learning Courses Hyperparameter Optimization Courses Data Augmentation Courses Stochastic Gradient Descent Courses

Course Description

Overview

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Explore two innovative algorithms, DP-RAFT and DOPE-SGD, designed to enhance the privacy-utility tradeoff in Differentially Private Machine Learning using public data. Learn how these techniques leverage available public information to improve model accuracy while maintaining strong privacy guarantees. Discover the benefits of DP-RAFT for scenarios with ample public data for pretraining, and understand how DOPE-SGD utilizes advanced data augmentation to maximize limited public data resources. Gain insights into overcoming the challenges of gradient clipping and noise addition in Differentially Private Stochastic Gradient Descent (DP-SGD) to achieve better model performance without compromising privacy protection.

Syllabus

Improving the Privacy Utility Tradeoff in Differentially Private Machine Learning with Public Data


Taught by

Google TechTalks

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