Sure but the output would still leak data. E.g: You give the RNN the phrase "Chase Bank" and it outputs "Sell the stock".
Encrypting the data secures the data loading part but it's not like your model didn't learn anything.
Encrypting the data secures the data loading part but it's not like your model didn't learn anything.
There might be encryption schemes which make learning such a "language" harder for an rnn, but it does not necessarily mean these methods are harder to break. So it might be possible to find/design an encryption method which is easy to learn (map x to y), but hard to break. I don't know much about encryption though, so I might be wrong.
Proof: You can easily train an RNN to return the n-th letter of its input and ignore everything else. Take that function to be f_n. Then g_n is the function that takes the encrypted input and returns the n-th letter, encrypted. If the encryption scheme is learnable in the above sense, then you can also train an RNN to perform that task. You can use multiple of those RNNs to extract an encryption of each individual letter in the input, which is equivalent to having the input encrypted with a substitution cipher. Substitution ciphers are so easy to break with frequency analysis that people have been doing it by hand for more than a thousand years.
Conversely, if you have a secure encryption scheme, then all statistical regularities in the input should be obfuscated so that they cannot be exploited by an RNN.