Error correction in quantum cryptography based on artificial neural networks

Niemiec, Marcin

doi:10.1007/s11128-019-2296-4

Cited by 52 publications

(32 citation statements)

References 38 publications

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“…Concerning improvements to initial TPM network infrastructure, Gomez et al [31] observed that the synchronization period is reduced from 1.25 ms to less than 0.7 ms with an initial assignment of the weights between 15% to 20%. Niemiec [32] proposed a new concept for the main quantity reconciliation process using TPM networks. Dong and Huang [33] proposed a complex value-based neural network for neural cryptography.…”

Section: Related Workmentioning

confidence: 99%

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

et al. 2021

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In this article, a whale optimization-based neural synchronization has been proposed for the development of the key exchange protocol. At the time of exchange of sensitive information, intruders can effortlessly perform sniffing, spoofing, phishing, or Man-In-The-Middle (MITM) attack to tamper the vital information. Information needs to be secretly transmitted with high level of encryption by preserving the authentication, confidentiality, and integrity factors. Such stated requirements urge the researchers to develop a neural network-based fast and robust security protocol. A special neural network structure called Double Layer Tree Parity Machine (DLTPM) is proposed for neural synchronization. Two DLTPMs accept the common input and different weight vectors and update the weights using neural learning rules by exchanging their output. In some steps, it results in complete synchronization, and the weights of the two DLTMs become identical. These identical weights serve as a secret key. There is, however, hardly any research in the field of neural weight vector optimization using a nature-inspired algorithm for faster neural synchronization. In this article, whale optimization-based DLTPM is proposed. For faster synchronization, this proposed DLTPM model uses a whale algorithm optimized weight vector. This proposed DLTPM model is faster and has better security. This proposed technique has been passed through a series of parametric tests. The results have been compared with some recent techniques. The results of the proposed technique have shown effective and has robust potential.The associate editor coordinating the review of this manuscript and approving it for publication was Jiafeng Xie.secret while he may be able to follow the structure of the algorithm. Chen et al. [2]; Liu et al. [3]; Chen et al. [4]; Wang et al. [5], [6]; Xiao et al. [7]; Zhang and Cao [8]; Wang et al. [9]; Dong et al. [10] described that the neural network synchronization approach offers the chance to solve enormous exchange problems. Rosen-Zvi et al. [11]; Lakshmanan et al. [12] ; Ni and Paul [13] recently showed that neural cryptography has the capability of achieving key exchange through neural synchronization of Artificial Neural Networks (ANN). This proposed technique uses an ANN called the Double Layer Tree Parity Machine (DLTPM). By generating common inputs and sharing the outputs of such networks,

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Section: Related Workmentioning

confidence: 99%

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

et al. 2021

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“…The number of steps is also decreased from 220 to less than 100. Niemiec [36] is proposing a new concept for the main quantity reconciliation process using TPM networks. Dong and Huang [14] proposed a complex value-based neural network for neural cryptography.…”

Section: Related Workmentioning

confidence: 99%

Generative adversarial network guided mutual learning based synchronization of cluster of neural networks

Sarkar

2021

Complex Intell. Syst.

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Neural synchronization is a technique for establishing the cryptographic key exchange protocol over a public channel. Two neural networks receive common inputs and exchange their outputs. In some steps, it leads to full synchronization by setting the discrete weights according to the specific rule of learning. This synchronized weight is used as a common secret session key. But there are seldom research is done to investigate the synchronization of a cluster of neural networks. In this paper, a Generative Adversarial Network (GAN)-based synchronization of a cluster of neural networks with three hidden layers is proposed for the development of the public-key exchange protocol. This paper highlights a variety of interesting improvements to traditional GAN architecture. Here GAN is used for Pseudo-Random Number Generators (PRNG) for neural synchronization. Each neural network is considered as a node of a binary tree framework. When both i-th and j-th nodes of the binary tree are synchronized then one of these two nodes is elected as a leader. Now, this leader node will synchronize with the leader of the other branch. After completion of this process synchronized weight becomes the session key for the whole cluster. This proposed technique has several advantages like (1) There is no need to synchronize one neural network to every other in the cluster instead of that entire cluster can be able to share the same secret key by synchronizing between the elected leader nodes with only logarithmic synchronization steps. (2) This proposed technology provides GAN-based PRNG which is very sensitive to the initial seed value. (3) Three hidden layers leads to the complex internal architecture of the Tree Parity Machine (TPM). So, it will be difficult for the attacker to guess the internal architecture. (4) An increase in the weight range of the neural network increases the complexity of a successful attack exponentially but the effort to build the neural key decreases over the polynomial time. (5) The proposed technique also offers synchronization and authentication steps in parallel. It is difficult for the attacker to distinguish between synchronization and authentication steps. This proposed technique has been passed through different parametric tests. Simulations of the process show effectiveness in terms of cited results in the paper.

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“…e synchronization between two neural networks can also be used as error reconciliation in quantum key distribution protocols [23]. Niemiec [24] proposed a novel method that the tree parity machine is used to correct errors taken place during transmission in quantum key distribution protocol. e influence of parameter variation on security and the influence of different learning rules on efficiency are analyzed in [25].…”

Section: Related Workmentioning

confidence: 99%

An Improved Method to Evaluate the Synchronization in Neural Key Exchange Protocol

Han¹,

Liu²,

Li³

et al. 2020

Security and Communication Networks

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The synchronization between two neural networks by mutual learning can be used to design the neural key exchange protocol. The critical issue is how to evaluate the synchronization without a weight vector. All existing methods have a delay in evaluating the synchronization, which affects the security of the neural key exchange. To evaluate the full synchronization of neural networks more timely and accurately, an improved method for evaluating the synchronization is proposed. First, the frequency that the two networks have the same output in previous steps is used for assessing the degree of them roughly. Second, the hash function is utilized to judge whether the two networks have achieved full synchronization precisely when the degree exceeds a given threshold. The improved method can find the full synchronization between two networks with no information other than the hash value of the weight vector. Compared with other methods, the full synchronization can be detected earlier by two communication partners which adopt the method proposed in this paper. As a result, the successful probability of geometric is reduced. Therefore, the proposed method can enhance the security of the neural exchange protocol.

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Error correction in quantum cryptography based on artificial neural networks

Cited by 52 publications

References 38 publications

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

Artificial Neural Synchronization Using Nature Inspired Whale Optimization

Generative adversarial network guided mutual learning based synchronization of cluster of neural networks

An Improved Method to Evaluate the Synchronization in Neural Key Exchange Protocol

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