4 ms·
Yes, it’s rather surprising that it can be done at all. And no, you train the model normally and then express the application of the model in terms of the set o
by StefanKarpinski 7y ago
Yes, it’s rather surprising that it can be done at all. And no, you train the model normally and then express the application of the model in terms of the set of primitive operations that the encryption scheme supports. Broadening the set of operations supported by the encryption scheme is an active area of research since having a better “instruction set” allows more computations to be done efficiently.
It is also possible for the model to be provided by the user and encrypted along with the data rather than being fixed and public. That way the service can apply a model that it doesn't know to data that it also doesn't know. This allows a fairly general service in theory, but it remains a challenge to express more than fairly simple models in a way that can be computed efficiently.
- pishpash 7y agoThis depends on a very special kind of "encryption".
- de_watcher 7y agoI don't understand why it has to be trained on encrypted data. Isn't it faster to train on the cleartext data (and of course use the same instruction set, the unencrypted counterpart of it)?
- mikorym 7y agoI think he is saying that you do the training on cleartext data.
- de_watcher 7y agoAh ok, his first "yes" confused me so much that I saw no value to read further.
- mrmonkeyman 7y agoAll those letters, those words. Terribly confusing.
- mikorym 7y agoI would think the pre-trained point is critical. Is there a way of doing the training (without being provided a trained model) all on the encrypted side?
- garganzol 7y agoYes, but the data owner will need to provide the feedback in clear text.
- k__ 7y agoDoes this has something to do with all the operations being linear?
- madrafi 7y agoNot really ! Activation functions are usually non-linear. What happens is we turn our numbers into their counterparts in another group (abstract group) and work there (at a high-level).