title: Learning to Protect Communications with Adversarial Neural Cryptography
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.
  
authors: Martín Abadi David G. Andersen
hIndex: Martín Abadi 11