3 ms·
It would be good to see a proof why the homomorphism property is sufficient for doing ML (or a specific type of ML) on encrypted data. Intuitively it may or ma
by mikorym 7y ago
It would be good to see a proof why the homomorphism property is sufficient for doing ML (or a specific type of ML) on encrypted data.
Intuitively it may or may not make sense to people, but a proof would also clarify whether there are any caveats, limitations or other particuliarities.
- ummonk 7y agoIt is really straightforward. The currently top voted comment basically explains it. If you take data x and encrypt it with encryption function C to get C(x), and then apply your machine learning model f to that to get f(C(x)), homomorphism means that f(C(x)) = C(f(x)) (because f and C commute), so the client can then apply the decryption to the result to get C-1(f(C(x)) = C-1(C(f(x)) = f(x).
- ct520 7y agomore like really straight forward over my head. I appreciate you taking the time to explain it - still can't grasp the concept. Back to framework connecting.
- deleted 7y ago[deleted]
- xvector 7y agoYou might want to clarify that “C-1” is actually C^-1 because otherwise I was wondering where subtraction comes into play.
- ummonk 7y agoWhoops, too late to edit; thanks for posting the clarification.
- mikorym 7y agoBut are you saying that a ML model is just a function? The function C is clear, but what is f? What is the domain and what is the codomain? If f and C commute, then x in X is just your "data" and both the domain and codomain. Edit: > the top voted comment Ah, I see that the top comment says that you use a pre-trained model. Intuitively that I think makes sense. Bot how about training the model itself?