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Side Channel Attack on a Quantum Random Number Generator

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Alexandr Kuznetsov; Oleksii Nariezhnii; Igor Stelnyk; Tetiana Kokhanovska; Oleksii Smirnov; Tetiana Kuznetsova
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Abstract:

The paper presents the results of applying an advanced attack on an external channel to an extractor of a quantum random number generator (QRNG). The attack is based on t...Show More

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Abstract:

The paper presents the results of applying an advanced attack on an external channel to an extractor of a quantum random number generator (QRNG). The attack is based on the verification of mathematical models of the interception channel and the EM signal of the extractor. Numerical simulation of the electromagnetic (EM) signal and the master key interception channel was performed based on the implementation of the DEMA algorithm of the attack on the AES-128 cipher. Measurements of the parameters of the EM signal and the interception channel were carried out on an experimental setup using a modern reference base in the field of electromagnetic compatibility.
Published in: 2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
Date of Conference: 18-21 September 2019
Date Added to IEEE Xplore: 05 December 2019
ISBN Information:
DOI: 10.1109/IDAACS.2019.8924447
Conference Location: Metz, France
References is not available for this document.
Contents

I. Introduction

Generation of random numbers based on quantum processes is one of the most important and important tasks of cryptography [1], [2]. In the scheme of optical pumping and resonance detection, only two types of noise are fundamentally quantum in nature – the fractional noise of light and the quantum noise of an atomic ensemble [3]–[6]. It is known that the quantum phase noise of an atomic ensemble has a Gaussian probability distribution law [6]. Therefore in quantum random number generators (QRNG) use a special means of enhancing entropy – a quantum extractor. The initial sequences of random numbers after the QRNG extractor successfully pass the DIEHARD and NIST STS statistical tests [7].

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