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- W2545316382 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." @default.
- W2545316382 created "2016-11-04" @default.
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- W2545316382 date "2016-10-21" @default.
- W2545316382 modified "2023-10-03" @default.
- W2545316382 title "Learning to Protect Communications with Adversarial Neural Cryptography" @default.
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