九州大学 · 情報科学
Ishak Meraouche教授の研究室では、人工知能を応用した暗号技術、特にGAN（敵対的生成ネットワーク）を基盤とする神経ネットワーク型暗号の研究が進められています。2016年のアドバーシャル暗号の提唱を契機に、神経ネットワークがワンタイムパッドに相当する完全な情報理論的セキュリティを達成できる可能性が示され、その研究が活発化しています。特に、複数のネットワークが相互に同期しながら安全な通信を確立する分散型暗号プロトコルの構築が主な研究テーマです。
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A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson’s work on adversarial cryptography in 2016 rejuvenated the research area which now focuses in building neural networks that are able to learn cryptography using the idea from Generative Adversarial Networks (GANs). In this paper, we survey the most prominent research works that cover neural networks based cryptograp
Neural networks based cryptography has seen a significant growth since the introduction of adversarial cryptography which makes use of Generative Adversarial Networks (GANs) to build neural networks that can learn encryption. The encryption has been proven weak at first but many follow up works have shown that the neural networks can be made to learn the One Time Pad (OTP) and produce perfectly secure ciphertexts. To the best of our knowledge, existing works only considered communications betwee
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