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Aspects of Neural Cryptography

Artificial Intelligence for Encryption and Cryptanalysis

  1. Cryptanalysis
  2. Stochastic
  3. Artificial neural network (ANN)
  4. Encryption

Cryptanalysis

Given some encrypted data (“ciphertext”), the goal of the cryptanalyst is to gain as much information as possible about the original, unencrypted data (“plaintext”).

It is useful to consider two aspects of achieving this.

  1. The first is breaking the system — that is discovering how the encipherment process works.
  2. The second is solving the key that is unique for a particular encrypted message or group of messages.

Stochastic

This is simply a fancy way to say random. The word meant random. Stochastic refers to a randomly determined process. The word first appeared in English to describe a mathematical object called a stochastic process, but now in mathematics the terms stochastic process and random process are considered interchangeable.

Artificial neural network (ANN)

Artificial neural networks or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems “learn” to perform tasks by considering examples, generally without being programmed with task-specific rules.

Encryption

In cryptography, encryption is the process of encoding a message or information in such a way that only authorized parties can access it and those who are not authorized cannot. Encryption does not itself prevent interference, but denies the intelligible content to a would-be interceptor.

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AI Policy, Governance, Ethics and International Partnerships at www.nora.ai. All views are my own. twitter.com/AlexMoltzau

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Alex Moltzau

AI Policy, Governance, Ethics and International Partnerships at www.nora.ai. All views are my own. twitter.com/AlexMoltzau