72 citations · 394 across the 35 of their papers we have counts for
4 papers · 1 filter
Multi-Central Differential Privacy
Thomas Steinke
Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their…
New Oracle-Efficient Algorithms for Private Synthetic Data Release
Giuseppe Vietri, Grace Tian, Mark Bun +2
We present three new algorithms for constructing differentially private synthetic data---a sanitized version of a sensitive dataset that approximately preserves the answers to a la…
The Discrete Gaussian for Differential Privacy
Clément L. Canonne, Gautam Kamath, Thomas Steinke
A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous dist…
Reasoning About Generalization via Conditional Mutual Information
Thomas Steinke, Lydia Zakynthinou
We provide an information-theoretic framework for studying the generalization properties of machine learning algorithms. Our framework ties together existing approaches, including…