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IEEE Access

A Modified Key Sifting Scheme With Artificial Neural Network Based Key Reconciliation Analysis in Quantum Cryptography

Chitra Biswas, Md. Mokammel Haque, Udayan Das Gupta

Published Jan 1, 2022• 22 citations
Publisher

Abstract

Quantum Cryptography emerged from the limitations of classical cryptography. It will play a vital role in information security after the availability of expected powerful quantum computers. Still many quantum primitives like quantum money, blind quantum computation, quantum copy protection, etc. are theoretical as they require a completely functional quantum computer for their implementation. But one prominent quantum cryptographic primitive, the Quantum Key Distribution (QKD) is possible with current technology. The QKD is a key establishment system having several stages namely raw key generation, key sifting, key reconciliation, and privacy amplification. In this paper, an efficient key sifting scheme has been developed. Successful simulation has shown that the proposed modified key sifting scheme requires less time to build the sifted key compared to the sifted key in conventional BB84 protocol in most cases. This paper also represents Tree Parity Machine (TPM) based key reconciliation analysis using different learning algorithms such as Hebbian, Anti-Hebbian, and Random-Walk. This reconciliation analysis helps to choose the optimum learning algorithm for Artificial Neural Network (ANN) based key reconciliation in future Quantum Key Distribution systems.

DOI
10.1109/access.2022.3188798

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Quantum Cryptography emerged from the limitations of classical cryptography. It will play a vital role in information security after the availability of expected powerful quantum computers. Still many quantum primitives like quantum money, blind quantum computation, quantum copy protection, etc. are theoretical as they require a completely functional quantum computer for their implementation. But one prominent quantum cryptographic primitive, the Quantum Key Distribution (QKD) is possible with current technology. The QKD is a key establishment system having several stages namely raw key generation, key sifting, key reconciliation, and privacy amplification. In this paper, an efficient key sifting scheme has been developed. Successful simulation has shown that the proposed modified key sifting scheme requires less time to build the sifted key compared to the sifted key in conventional BB84 protocol in most cases. This paper also represents Tree Parity Machine (TPM) based key reconciliation analysis using different learning algorithms such as Hebbian, Anti-Hebbian, and Random-Walk. This reconciliation analysis helps to choose the optimum learning algorithm for Artificial Neural Network (ANN) based key reconciliation in future Quantum Key Distribution systems.
Information Security is of paramount importance in digital communication in today’s highly networked world. Conventional information security methods face many future threats, one being the likelihood of large scale Quantum Computation. Quantum Cryptography is coming to the fore in the field of information security. It is all about using quantum physics to perform the cryptographic tasks as well as to break cryptographic systems. There are recent many advancements in the quantum cryptography field like Quantum Coin Flipping, Quantum Bit Commitment, and Quantum Key Distribution (QKD) networks. In this paper, a new concept for error correction and key reconciliation method is presented that uses Artificial Neural Network (ANN) with the help of Tree Parity Machine (TPM). In our work, we have considered a few cases of TPMs synchronization process with specific values of Quantum Bit Error Rate(QBER) and have performed some comparison among those. The experimental results observed into this paper that prove the correctness of the proposed work.
Cloud storage has gained extensive attention due to its low cost, global scalability, and improved agility.Data privacy is an important issue in cloud storage because breaches of personal information or business secrets are occurred by malicious hackers or unauthorized cloud operators.To address this issue, a Modified Key Sifting Scheme (MKSS) is proposed for developing an effective Quantum Key Distribution (QKD) with Two-Way Identity Authentication (TWIA) to provide security in the cloud storage.The Tree Parity Machine (TPM) is used for proposing a MKSS which used to minimize the key distribution time based on the synchronization.This MKSS-QKDTWIA considers four different objects such as data user, data owner, server and data storage in a communication scenario.The performance of the MKSS-QKDTWIA is analyzed by means of key computation time, processing time, encryption & decryption time, and memory utilization.Existing research such as Modified Random Fibonacci Cryptographic (MRFC), Software Stack-QKD (SS-QKD) and Enhanced Cloud Security Model based on QKD Protocol (ECSM-QKDP) are used to evaluate the performance of MKSS-QKDTWIA.The key computation time of MKSS-QKDTWIA for 50 attribute sets in each key is 16.7s which is less when compared to the MRFC, SS-QKD and ECSM-QKDP.
Data protection and information security have been the essence of communication in today's digital era. Authentication and secrecy of secure communication are achieved using key-based cryptographic primitives; the security of which significantly relies upon the underlying computationally complex mathematics. Moreover, these existing cryptographic primitives are considered to be non-deterministic on the basis of the existing computational capabilities. However, the considerable advancements in the development of quantum computers have significantly enhanced parallel computations; thereby, posing a great threat to these existing encryption primitives. Thus, in the future, the physical manifestation of a large successful quantum computer is likely to break all the existing public-key encryption algorithms in no time. This has led to a remarkable surge of interest in propelling quantum mechanics into existence; subsequently, leading cryptographers to research various viable domains to offer quantum-resistant secure communications. Resultantly, quantum cryptography/quantum key distribution has emerged as a futuristic replacement for classical cryptography as it offers unconditionally secure communication along with the inherent detection of any unintended user. Thus, keeping in view the significance of this relatively newer domain of cryptography, this research focuses on presenting a consolidated review of the various Quantum Key Distribution (QKD) protocols. A comparative analysis of the working mechanism of the prominent QKD protocols is presented along with an overview of the various emerging trends that have been proposed to optimize the implementational efficiency of the BB84 protocol.
The article discusses a key reconciliation protocol for quantum key distribution (QKD) systems based on Tree Parity Machines (TPM). The idea of transforming key material into neural network weights is presented. Two experiments were conducted to study how the number of synchronization iterations and the amount of leaked information depend on the quantum bit error rate (QBER) and the range of neural network weights. The results show a direct relationship between the average number of synchronization iterations and QBER, an increase in iterations when the weight range is expanded, and a reduction in leaked information as the weight range increases. Based on these results, conclusions are drawn regarding the applicability of the protocol and the prospects for further research on neural cryptographic methods in the context of key reconciliation.
Quantum cryptography has emerged as a radical research field aimed at mitigating various security threats in modern communication systems. The integration of Quantum Machine Learning (QML) protocols plays a crucial role in enhancing security measures, addressing previously inaccessible threats, and improving cryptographic efficiency. Key research areas in quantum cryptography include Quantum Key Distribution (QKD), eavesdropping detection, QSDC, security analysis of QKD protocols, post-quantum cryptography, Quantum Network Security & Intrusion Detection, Quantum-secure communication beyond QKD, quantum random number generation, Quantum Secure Multi-Party Computation (QSMPC), Quantum Homomorphic Encryption (QHE), and privacy-preserving computation. QML algorithms improve the key generation of QKD, by improving quantum state selection and reducing measurements. This also allows them to increase efficiency because it identifies trends in errors and applies corrections, making quantum cryptography a more dependable option. With intelligent processing machine learning is excellent at handling complex, high-dimensional data-this may provide a viable strategy for enhancing QKD performance and increasingly real-world secure quantum communication networks. This review will explore current research gaps and future developments in QKD, security analysis of QKD protocols, and eavesdropping detection by leveraging various QML algorithms.
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 problems 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.