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2012 NIPS NeurIPS 2012

A Simple and Practical Algorithm for Differentially Private Data Release

Abstract

We present a new algorithm for differentially private data release, based on a simple combination of the Exponential Mechanism with the Multiplicative Weights update rule. Our MWEM algorithm achieves what are the best known and nearly optimal theoretical guarantees, while at the same time being simple to implement and experimentally more accurate on actual data sets than existing techniques.

🌉 Interdisciplinary Bridge — Machine Learning and Security & Privacy
📈 Trend Setter — Privacy
🧭 Keyword Pioneer — data release
🐣 Hot Topic Early Bird — differential privacy
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Deep Learning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy

Authors

Moritz Hardt , Katrina Ligett , Frank Mcsherry

Topics

Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Privacy Security & Privacy > Differential Privacy Machine Learning > Learning Types > Privacy

Keywords

differential privacy data release exponential mechanism privacy-preserving data release private data release data privacy multiplicative weight
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