Google rolls out open source version of its differential privacy library

The library is meant to help developers build products that utilize anonymized aggregate data in a "privacy-preserving manner".

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Google has released an open-source version of the differential privacy library that's used to power some of its core products. The library is meant to help developers build products that utilize anonymized aggregate data in a "privacy-preserving manner".

"Whether you're a city planner, a small business owner, or a software developer, gaining useful insights from data can help make services work better and answer important questions," wrote Miguel Guevara, a product manager in Google's Privacy and Data Protection Office. "But, without strong privacy protections, you risk losing the trust of your citizens, customers, and users. Differentially-private data analysis is a principled approach that enables organizations to learn from the majority of their data while simultaneously ensuring that those results do not allow any individual's data to be distinguished or re-identified."

Guevara notes that the C++ library focuses on features that are often difficult to execute from scratch, as well as standard statistical functions such as counts, sums, averages, medians, and variance. The library also includes a PostgreSQL extension along with some commonly used recipes to help developers get started.

"We're excited to make this library broadly available and hope developers will consider leveraging it as they build out their comprehensive data privacy strategies," Guevara added. "From medicine to government, to business, and beyond, it's our hope that these open-source tools will help produce insights that benefit everyone."

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