Google's Privacy Library Is Now Open Source

September 10, 2019

Google, following in the footsteps of other tech giants, has published its open-source differential privacy library on GitHub. 

Let’s talk about trust

We live in an era in which there are two competing trends. On the one hand, big data is the name of the game, and the internet giants (Google, Amazon, etc.) are those mining this data, often of a personal nature. On the other hand, the public is pushing for transparency and trust, wanting to be confident that their private data remains private and is not being unknowingly shared.

Differential privacy can make all the difference

Enter differential privacy. Differential privacy is a data science technique that adds white noise to data sets, obfuscating personally identifiable information. This allows researchers, organizations, or whoever might be interested in data analysis to derive insights, while avoiding violating privacy.

Google, Apple, and Uber, are using this method to analyze personal data from their customers in a privacy-first manner. Although Apple’s proprietary tools are kept under lock and key, Uber has open-sourced its differential privacy library  and this week, Google has followed suit, releasing its own open-source differential privacy library for the world to use and improve. 

Google has used this library to improve a number of its products, including Google Maps. And the possibilities of the differential privacy approach are far-reaching. For example, as Google points out in its announcement, it could be used by health researchers looking at the amount of time patients are spending in hospitals.

Saving private data

So, maybe you can have it both ways: having your data and keeping it private, too. 


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Google's Privacy Library Is Now Open Source Google continues its privacy push, publishing its open-source differential privacy library on GitHub for the world to use and improve.
Matthew Miller Matthew Miller is a research and data enthusiast with a knack for understanding and conveying market trends effectively. With experience in journalism, education, and AI, he has honed his skills in various industries. Currently a Senior Research Analyst at G2, Matthew focuses on AI, automation, and analytics, providing insights and conducting research for vendors in these fields. He has a strong background in linguistics, having worked as a Hebrew and Yiddish Translator and an Expert Hebrew Linguist, and has co-founded VAICE, a non-profit voice tech consultancy firm.