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.
Description
Learning to Protect Communications with Adversarial Neural Cryptography
%0 Generic
%1 abadi2016learning
%A Abadi, Martín
%A Andersen, David G.
%D 2016
%K Adversarial Cryptography Neural
%T Learning to Protect Communications with Adversarial Neural Cryptography
%U http://arxiv.org/abs/1610.06918
%X 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.
@misc{abadi2016learning,
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.},
added-at = {2017-10-04T16:32:09.000+0200},
author = {Abadi, Martín and Andersen, David G.},
biburl = {https://www.bibsonomy.org/bibtex/23e6082a931db3ec3f6065d7d83e1b8ba/daschloer},
description = {Learning to Protect Communications with Adversarial Neural Cryptography},
interhash = {f54f6fc9afb36adb6a0c781e1b2a2af5},
intrahash = {3e6082a931db3ec3f6065d7d83e1b8ba},
keywords = {Adversarial Cryptography Neural},
note = {cite arxiv:1610.06918Comment: 15 pages},
timestamp = {2017-10-04T16:32:09.000+0200},
title = {Learning to Protect Communications with Adversarial Neural Cryptography},
url = {http://arxiv.org/abs/1610.06918},
year = 2016
}