Privacy-preserving A.I. is the future of A.I.


Jeremy Kahn

I spent part of last week listening to the panel discussions at CogX, the London “festival of A.I. and emerging technology” that takes place each June. This year, due to Covid-19, the event took place completely online. (For more about how CogX pulled that off, look here.)

There were tons of interesting talks on A.I.-related topics. If you didn't catch any of it, I'd urge you to look up the agenda and try to find recordings of the sessions on YouTube.

One of the most interesting sessions I tuned into was on privacy-preserving machine learning. This is becoming a hot topic, particularly in healthcare, and especially now due to the interest in applying machine learning to healthcare records that the coronavirus pandemic is helping to accelerate.

Currently, the solution to preserving patient privacy in most datasets used for healthcare A.I. is to anonymize the data: In other words, personal identifying information such as names, addresses, phone numbers, and social security numbers is simply stripped out of the dataset before it is fed to the A.I. algorithm. Anonymization is also the standard in other industries, especially those that are heavily regulated, such as finance and insurance.

But researchers have shown that this kind of anonymization doesn’t guarantee privacy: There are often other fields in data, such as...

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