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Zetta Bytes: Privacy Preserving Machine Learning

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Dear All,

We’re diving into ten key areas for innovation as a part of a monthly series on AI-enabled applications and the technological breakthroughs needed to support them. Thus far, we’ve written about six areas of innovation and today, we’re offering a seventh: privacy preserving machine learning.

In the age of nearly constant data breaches, data privacy has become a defining issue for tech regulators, leading to sweeping laws like GDPR in Europe and San Francisco’s recent decision to ban facial recognition software. This poses a particular challenge for machine learning where user data is collected and pooled in the cloud in order to train intelligent systems. However, a new class of privacy-preserving machine learning architectures such as ‘Federated Learning’ could hold the key to maintaining user privacy while allowing models to train on real world data.

In federated learning, a model is downloaded to an edge device — like a mobile phone — where it runs locally and sends a periodic summary of its learnings as an encrypted message to the cloud. There, thousands of individual summaries are averaged together to update the model without user data ever leaving the device. Originally developed at Google in 2017 to power keyboard recommendations, federated learning since inspired a range of new techniques and applications.

In particular, privacy-preserving ML is catching on in healthcare where it’s being used to read EEGs, develop drugs and predict cardiac arrest through smart speakers at home. Other groups, concerned with device-level vulnerabilities, are combining federated learning with encrypted hardware and are working with major healthcare systems like Stanford.

It’s still early days for the technology — with plenty of performance limitations and security vulnerabilities to work out — but we expect to see many more applications of privacy-preserving machine learning in the coming decade.

Warmest regards, Mark, Jocelyn, Ash, Dylan, James and Todd


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