Healthcare involves the exchange of unsecured information between two people, right? After all, how could a doctor possibly help you to stay healthy, without knowing anything about you. But things are changing.
There are two major intersecting trends. First, computers double their compute performance every year or two and are beginning to rival and exceed human performance in multiple clinical specialties, such as radiology and dermatology. This allows us to broaden our views of who, or what, doctors are. Second, it’s possible to compute on encrypted data, such that only the person who generated the data can see the computation results.
When you combine those two things – powerful classifiers and ability to compute on encrypted data – you end up with something new. You can begin to imagine a world where healthcare is both affordable, costing fractions of a penny per diagnosis, and completely private. In the last few months, we’ve built the world’s largest deployment of Secure Health, in which computers work on encrypted data to give people useful insights, in this case, a personalized COVID risk estimate.
We’ve also decided to make the data we obtained from millions of people around the world available to scientists and doctors, as a starting point to further discovery and impact.
The preprint is out: https://www.medrxiv.org/content/10.1101/2020.09.23.20200006v2
This is only possible because millions of people in 91 countries thought that this was a good idea, and took a leap of faith to share their symptoms and test results with the FeverIQ efforts, which uses Enya’s secure multiparty computation API to classify and learn without their data ever leaving their phone. Thank you, to each one of you.
Biology, Data, and Networks 