Error Reconciliation in Quantum Key Distribution Protocols

Mehić, Miralem; Niemiec, Marcin; Šiljak, Harun; Vozňák, Miroslav

doi:10.1007/978-3-030-47361-7_11

Cited by 34 publications

(15 citation statements)

References 31 publications

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“…Unfortunately, it requires large block lengths (

) [ 40 ]. Even more, decoding LDPC has larger computational and memory requirements than either Cascade or Winnow algorithms [ 41 ]. In contrast, our method does not require additional bits which reduces the coding rate.…”

Section: Communication Modelmentioning

confidence: 99%

Beyond the Limits of Shannon’s Information in Quantum Key Distribution

Lizama-Pérez

Samperio

2021

Entropy

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We present a new post-processing method for Quantum Key Distribution (QKD) that raises cubically the secret key rate in the number of double matching detection events. In Shannon’s communication model, information is prepared at Alice’s side, and it is then intended to pass it over a noisy channel. In our approach, secret bits do not rely in Alice’s transmitted quantum bits but in Bob’s basis measurement choices. Therefore, measured bits are publicly revealed, while bases selections remain secret. Our method implements sifting, reconciliation, and amplification in a unique process, and it just requires a round iteration; no redundancy bits are sent, and there is no limit in the correctable error percentage. Moreover, this method can be implemented as a post-processing software into QKD technologies already in use.

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“…Unfortunately, it requires large block lengths (

Section: Communication Modelmentioning

confidence: 99%

Beyond the Limits of Shannon’s Information in Quantum Key Distribution

Lizama-Pérez

Samperio

2021

Entropy

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“…Shishniashvili et al [38] suggested a strategy to make use of the portions of the synchronized weight as a session key rather than the whole weight vector. In quantum key distribution protocols, Mehic et al [20] synchronization of two neural nets can serve as a reconciliation of errors as well. Niemiec [21] suggested the new approach of using the tree parity machine to address the mistakes in the quantum key distribution protocol during transmission.…”

Section: Related Workmentioning

confidence: 99%

Mutual learning-based efficient synchronization of neural networks to exchange the neural key

Sarkar

2021

Complex Intell. Syst.

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Synchronization of two neural networks through mutual learning is used to exchange the key over a public channel. In the absence of a weight vector from another party, the key challenge with neural synchronization is how to assess the coordination of two communication parties. There is an issue of delay in the current techniques in the synchronization assessment that has an impact on the security and privacy of the neural synchronization. In this paper, to assess the complete coordination of a cluster of neural networks more efficiently and timely, an important strategy for assessing coordination is presented. To approximately determine the degree of synchronization, the frequency of the two networks having the same output in prior iterations is used. The hash is used to determine if both the networks are completely synchronized exactly when a certain threshold is crossed. The improved technique makes absolute coordination between two communication parties using the weight vectors’ has value. In contrast, with existing approaches, two communicating parties who follow the proposed approach will detect complete synchronization sooner. This reduces the effective geometric likelihood. The proposed method, therefore, increases the safety of the protocol for neural key exchange. 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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“…Shishniashvili et al [29] recommended using components of the coordinated synaptic weight vector as a cryptographic key rather than a complete weight vector. In quantum key management procedures, coordination of two systems could be used to eliminate errors [12]. The complex-valued tree parity machine (CVTPM) was suggested by the authors in Dong and Huang [3], which utilizes complex numbers for all control parameters.…”

Section: Related Workmentioning

confidence: 99%

Chaos-guided neural key coordination for improving security of critical energy infrastructures

Sarkar

Khan

Noorwali

2021

Complex Intell. Syst.

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In this paper, chaos-guided artificial neural learning-based session key coordination for industrial internet-of-things (IIoT) to enhance the security of critical energy infrastructures (CEI) is proposed. An intruder might pose several security problems since the data are transferred across a public network. Although there have been substantial efforts to solve security problems in the IIoT, the majority of them have relied on traditional methods. A wide range of privacy issues (secrecy, authenticity, and access control) must be addressed to protect IIoT systems against attack. Owing to the unique characteristics of IIoT nodes, existing solutions do not properly address the entire security range of IIoT networks. To deal with this, a chaos-based triple layer vector-valued neural network (TLVVNN) is proposed in this paper. A chaos-based exchange of common seed value for the generation of the identical input vector at both transmitter and receiver is also proposed. This technique has several advantages, including (1) it protects IIoT devices by utilizing TLVVNN synchronization to improve CEI security. (2) Here, artificial neural coordination is utilized for the exchange of neural keys between two IIoT nodes. (3) Using this suggested methodology, chaotic synchronization can be achieved, enabling the chaos-based PRNG seed exchange. (4) Vector-valued inputs and weights are taken into consideration for TLVVNN networks. (5) The deep internal architecture is made up of three hidden layers of the neural network and a vector value as input. As a result, the attacker would have great difficulty interpreting the internal structure. Experiments to verify the performance of the proposed technique are conducted, and the findings demonstrate that the proposed technique has greater performance benefits than the existing related techniques.

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Error Reconciliation in Quantum Key Distribution Protocols

Cited by 34 publications

References 31 publications

Beyond the Limits of Shannon’s Information in Quantum Key Distribution

Beyond the Limits of Shannon’s Information in Quantum Key Distribution

Mutual learning-based efficient synchronization of neural networks to exchange the neural key

Chaos-guided neural key coordination for improving security of critical energy infrastructures

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