In recent years, neural networks have been used to implement symmetric cryptographic functions for secure communications. Extending this domain, the proposed approach explores the application of asymmetric cryptography within a neural network framework to safeguard the exchange between two communicating entities, i.e., Alice and Bob, from an adversarial eavesdropper, i.e., Eve. It employs a set of five distinct cryptographic keys to examine the efficacy and robustness of communication security against eavesdropping attempts using the principles of elliptic curve cryptography. The experimental setup reveals that Alice and Bob achieve secure communication with negligible variation in security effectiveness across different curves. It is also designed to evaluate cryptographic resilience. Specifically, the loss metrics for Bob oscillate between 0 and 1 during encryption-decryption processes, indicating successful message comprehension post-encryption by Alice. The potential vulnerability with a decryption accuracy exceeds 60\%, where Eve experiences enhanced adversarial training, receiving twice the training iterations per batch compared to Alice and Bob.

In the context of AI, homomorphic encryption could enable data scientists to perform operations on encrypted data that will yield the same results as if they were operating on clear data. Somewhere between anonymization methods and homomorphic encryption, we find a novel technique pioneered by Google that uses adversarial neural networks to protect information from other neural models. While that's true, it turns out that neural networks can learn to protect the confidentiality of their data from other neural networks: they discover forms of encryption and decryption, without being taught specific algorithms for these purposes. Alice and Bob have an advantage over Eve: they share a secret key K. That secret Key[K] is used as an additional input to Alice and Bob.

Last week I read Abadi and Andersen's recent paper [1], Learning to Protect Communications with Adversarial Neural Cryptography. I thought the idea seemed pretty cool and that it wouldn't be too tricky to implement, and would also serve as an ideal project to learn a bit more Theano. This post describes the paper, my implementation, and the results. The authors set up their experiment as follows. We have three neural networks, named Alice, Bob, and Eve.

Last week I read Abadi and Andersen's recent paper [1], Learning to Protect Communications with Adversarial Neural Cryptography. I thought the idea seemed pretty cool and that it wouldn't be too tricky to implement, and would also serve as an ideal project to learn a bit more Theano. This post describes the paper, my implementation, and the results. The authors set up their experiment as follows. We have three neural networks, named Alice, Bob, and Eve.