Leading the way in responsible innovation

We know the value that data has to your business, which is why we aim to help you gain insights from it in the most effective and responsible ways.

Your privacy is protected by responsible data practices.

Data plays an important role in making the products and services you use every day more helpful. We are committed to treating that data responsibly and protecting your privacy with strict protocols and innovative privacy technologies.

Privacy Reviews

Strict privacy protocols are followed throughout every product's development. Privacy is core to how we build our products, with rigorous privacy standards guiding every stage of product development. Each product and feature adheres to these privacy standards, which are implemented through comprehensive privacy reviews.

AI Ethics & Principles

AI has significant potential to help solve challenging problems, including by advancing medicine, understanding language, and fueling scientific discovery. To realize that potential, it's critical that AI is used and developed responsibly. To that end, we've established principles that guide Google AI applications, best practices to share our work with communities outside of Google, and programs to operationalize our efforts.

Helping make the online world safer.

Google chrome browser tabs with graphs

Google's COVID-19 Community Mobility Reports use world-class anonymization technology, including differential privacy, to keep user data private, safe, and secure.

Making technology for everyone means protecting everyone who uses it. We’re committed to building and sharing privacy and security technologies that protect our users and push the industry forward

Differential Privacy

Helping organizations anonymize data with differential privacy

Differential privacy is an advanced anonymization technology that allows anonymization technology that allows us to gain insights from data without compromising the anonymity of our users. WE spent over a decade building the largest library of differential privacy algorithms in the world and we open-sourced the library to help organizations easily apply the same privacy protections to their data

Resources for Developers

Federated Learning

Building helpful products with less data

Federated learning is a data minimization technology pioneered at Google that brings machine learning (ML) intelligence right to your device. This new approach combines anonymized information from different devices to train ML models. Federated learning helps preserve your privacy by keeping as much personal information on your device as possible.

Resources for developers

Private Join and Compute

Help organizations do more without collecting more data

Private Join and Compute keeps individuals safe while allowing organizations to accurately compute and draw useful insights from aggregate statistics. By sharing the technology more widely, we hope this expands the use cases for secure computing. We'll continue to invest in new research to advance innovations that preserve individual privacy while enabling valuable insights from data.

Resources for developers