I am pretty sure homomorphic encryption is not being used. In fact, I suspect that no real encryption is being used.
Isn't it the case that if I just removed the labels, and renormalized all my data to fall in [0, 1], then what I end up with looks a lot like what Numer.ai gives you?
I'm not aware of any homomorphic encryption / structure preserving schemes that have homomorphic evaluation on ciphertexts equivalent to literal multiplication and addition of ciphertexts, and this seems to be what they want you to do to train your model. (unless I'm misunderstanding how to interact with the "encrypted" dataset)
EDIT: seems like most people think they are using Order Preserving Encryption, which allows one to compare ciphertexts with the "less than" predicate. This makes more sense looking at what they give, but I never saw anything where they say "only do comparisons on the encrypted data."
Isn't it the case that if I just removed the labels, and renormalized all my data to fall in [0, 1], then what I end up with looks a lot like what Numer.ai gives you?
I'm not aware of any homomorphic encryption / structure preserving schemes that have homomorphic evaluation on ciphertexts equivalent to literal multiplication and addition of ciphertexts, and this seems to be what they want you to do to train your model. (unless I'm misunderstanding how to interact with the "encrypted" dataset)
EDIT: seems like most people think they are using Order Preserving Encryption, which allows one to compare ciphertexts with the "less than" predicate. This makes more sense looking at what they give, but I never saw anything where they say "only do comparisons on the encrypted data."