I'm interested in two following processes:
1) Perform deep learning on homomorphic encrypted data
2) Perform deep learning predictions with a homomorphic encrypted model on unencrypted data. By this I mean encrypting weights of a deep learning model, sending them to the owner of the data, and perform an encrypted prediction. The owner of the data will return back to me the encrypted prediction. See for instance this blog post for an example with logistic regression.
I'm wondering if it's possible to do deep learning (with many hidden layers) with PHE (partially homomorphic encryption, e.g. Paillier) or if I need a FHE (fully homomorphic encryption).
References are welcome!