Ravi Kumar

Google

[intermediate/advanced] Differential Privacy

Summary

This course will introduce differential privacy (DP) as an important tool for private analytics and machine learning.

Syllabus

DP: Motivation; Definition; Properties
Basic Mechanisms: Laplace; Gaussian; Exponential
Private Analytics: Histograms, Quantiles
Private ML: SGD; Hyperparameters
Local/Shuffle models: Properties; Algorithms
Advanced Methods: Synthetic data; Matrix mechanism

References

Pre-requisites

Basic probability. Basic machine learning.

Short bio

Ravi Kumar has been a research scientist at Google since 2012. Prior to this, he was at the IBM Almaden Research Center and at Yahoo! Research. His interests include algorithms for massive data, ML/privacy, and the theory of computation.

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