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Microsoft SmartNoise Differential Privacy Machine Learning Case Studies

This resource is available in English.

Published: 14/02/2021

The COVID-19 pandemic demonstrates the tremendous importance of data for research, cause analysis, government action, and medical progress. However, for understandable data protection considerations, individuals and decision-makers are often very reluctant to share personal or sensitive data. To ensure sustainable progress, we need new practices that enable insights from personal data while reliably protecting individuals' privacy. 

Pioneered by researchers at Microsoft in a collaboration with the OpenDP Initiative led by Harvard, differential privacy is the emerging gold standard for protecting data in applications like preparing and publishing statistical analyses. Differential privacy provides a mathematically measurable privacy guarantee to individuals by adding a carefully tuned amount of statistical noise to sensitive data. It promises significantly higher privacy protection levels than commonly used disclosure limitation practices like data anonymization. This whitepaper provides practical guidance on how personal data can be rigorously protected for applications like statistics, machine learning, and deep learning using Differential Privacy.