Title: Learning to Protect Communications with Adversarial Neural Cryptography
Authors: Martín Abadi, David G. Andersen
TL;DR: Adversarial training of neural networks to learn rudimentary forms of encryption with no pre-specified algorithms
Abstract:
We ask whether neural networks can learn to use secret keys to protect
information from other neural networks.  Specifically, we focus on
ensuring confidentiality properties in a multiagent system, and we
specify those properties in terms of an adversary.  Thus, a
system may consist of neural networks named Alice and Bob, and we aim
to limit what a third neural network named Eve learns from
eavesdropping on the communication between Alice and Bob.
We do not prescribe specific cryptographic algorithms to these neural networks;
instead, we train end-to-end, adversarially.
We demonstrate that the neural networks can learn 
how to perform forms of encryption and decryption, and also
how to apply these operations selectively in order to meet
confidentiality goals.

Venue: Submitted to ICLR 2017