I think what you're looking for is "Federated Learning" https://ai.googleblog.com/2017/04/federated-learning-collaborative.html This use case doesn't actually require homomorphic encryption; instead the ML training is split up between an on-device update stage, which uses the real data, and a global stage, which gets and combines model parameter updates from many devices, without leaking much information about any individual device's data set. --Michael On Sun, Jan 13, 2019 at 12:56 PM Henry Baker <hbaker1@pipeline.com> wrote:
Here's a real-world problem:
You have some sort of IoT (Internet of Things) sensor -- perhaps a camera -- and you want to train an AI/machine learning algorithm to recognize something that is exposed to the camera.
But perhaps you don't trust the AI/ML developer.
So you send him/her only an encrypted dataset along with the classification data (yes/no or perhaps a finite set of possibilities); this classification data isn't encrypted, and there isn't any easy way to figure out from the sequence of classifications any useful info about the encrypted dataset.
So far as I know, homomorphic encryption hasn't matured to the point where the entire training process could operate on homomorphically encrypted data.
But we're not talking here about completely generic calculations -- we're talking about quite limited calculations, just in enormous quantities (10^18 calculations).
Perhaps there are "homomorphic" encryption systems that do *just enough* and AI/ML systems that are dumbed down *just enough* that the two constraints can meet in the middle.
After all, AI/ML systems don't seem to care about most kinds of image distortions, so perhaps they could still be capable of characterizing certain pictures even after encryption ?
Obviously, if such things are possible, then there are clearly information leaks, but these might even be useful.
_______________________________________________ math-fun mailing list math-fun@mailman.xmission.com https://mailman.xmission.com/cgi-bin/mailman/listinfo/math-fun
-- Forewarned is worth an octopus in the bush.