Google Differential Privacy Library

A high-performance collection of C++, Java, and Go libraries for generating aggregate statistics with mathematical privacy guarantees.

Google's open-source library implements the Laplace and Gaussian mechanisms to protect individual data points within large datasets. It provides developers with ready-to-use algorithms for common operations (sum, average, variance, and count) while managing the privacy budget automatically. By adding calibrated noise to query results, the library prevents attackers from identifying specific users, even when combining multiple data sources. This toolkit powers privacy-centric features in Google Maps and the United States Census Bureau's data products.

https://github.com/google/differential-privacy