Synchronization of two tree parity machines

Javurek, Martin; Turčaník, Michal

doi:10.1109/ntsp.2016.7747782

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…The number of iterations is not fixed and depends on the size and parameters of the TPM. However, it is shown that the time is finite and can be easily estimated by users [18]. The process takes longer for larger TPM sizes (K and N) and maximum weight value (L).…”

Section: B Key Agreement Protocolmentioning

confidence: 99%

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Synchronization of Tree Parity Machines Using Nonbinary Input Vectors

Stypiński

Niemiec

2024

IEEE Trans. Neural Netw. Learning Syst.

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Neural cryptography is the application of artificial neural networks (ANNs) in the subject of cryptography. The functionality of this solution is based on a tree parity machine (TPM). It uses ANNs to perform secure key exchange between network entities. This brief proposes improvements to the synchronization of two TPMs. The improvement is based on learning ANN using input vectors that have a wider range of values than binary ones. As a result, the duration of the synchronization process is reduced. Therefore, TPMs achieve common weights in a shorter time due to the reduction of necessary bit exchanges. This approach improves the security of neural cryptography.

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Section: B Key Agreement Protocolmentioning

confidence: 99%

“…TPMs has been studied extensively. Javurek and M. Turčaník [18] and Martínez Padilla et al [20] identify four distinct types of attacks that TPM may be vulnerable to as follows.…”

Section: Security Of Tree Parity Machinesmentioning

confidence: 99%

Synchronization of Tree Parity Machines Using Nonbinary Input Vectors

Stypiński

Niemiec

2024

IEEE Trans. Neural Netw. Learning Syst.

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“…Taking into account the software environments and hardware resources currently available, the usage of TPM-based error correction seems to be justified. Although the time of learning processes for software implementations of TPMs strongly depends on the hardware resources, a typical duration of one iteration on an ordinary computer is a few milliseconds [39]. However, hardware implementation helps to shorten this time by more than thousandfold (i.e.…”

Section: Applicability Of Tpmsmentioning

confidence: 99%

“…However, error correction based on TPMs is able to equalize every number of incompatible bits between Alice and Bob's strings of bits and it works on any value of QBER. Also, it is worth remembering that we should use high values for TPM parameters (N, K, L) to ensure an even higher level of protection [39].…”

Section: Security Considerationsmentioning

confidence: 99%

Error correction in quantum cryptography based on artificial neural networks

Niemiec

2019

Quantum Inf Process

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Intensive work on quantum computing has increased interest in quantum cryptography in recent years. Although this technique is characterized by a very high level of security, there are still challenges that limit the widespread use of quantum key distribution. One of the most important problem remains secure and effective mechanisms for the key distillation process. This article presents a new idea for a key reconciliation method in quantum cryptography. This proposal assumes the use of mutual synchronization of artificial neural networks to correct errors occurring during transmission in the quantum channel. Users can build neural networks based on their own string of bits. The typical value of the quantum bit error rate does not exceed a few percent, therefore the strings are similar and also users' neural networks are very similar at the beginning of the learning process. It has been shown that the synchronization process in the new solution is much faster than in the analogous scenario used in neural cryptography. This feature significantly increases the level of security because a potential eavesdropper cannot effectively synchronize their own artificial neural networks in order to obtain information about the key. Therefore, the key reconciliation based on the new idea can be a secure and efficient solution.

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The Use of Genetic Algorithms for Cryptographic Keys Generation

Turčaník

Javurek

2021

Studies in Big Data

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Synchronization of two tree parity machines

Cited by 6 publications

References 3 publications

Synchronization of Tree Parity Machines Using Nonbinary Input Vectors

Synchronization of Tree Parity Machines Using Nonbinary Input Vectors

Error correction in quantum cryptography based on artificial neural networks

The Use of Genetic Algorithms for Cryptographic Keys Generation

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