Quantum Randomness in Cryptography—A Survey of Cryptosystems, RNG-Based Ciphers, and QRNGs

Abstract

Cryptography is the study and practice of secure communication with digital data and focuses on confidentiality, integrity, and authentication. Random number generators (RNGs) generate random numbers to enhance security. Even though the cryptographic algorithms are public and their strength depends on the keys, cryptoanalysis of encrypted ciphers can significantly contribute to the unveiling of the cipher’s key. Therefore, to ensure high data security over a network, researchers need to improve the randomness of keys as they develop cryptosystems. Quantum particles have a leading edge in advancing RNG technology as they can provide true randomness, unlike pseudo-random numbers generators (PRNGs). In order to increase the level of the security of cryptographic systems based on random numbers, this survey focuses on three objectives: Cryptosystems with related cryptographic attacks, RNG-based cryptosystems, and the design of quantum random number generators (QRNGs). This survey aims to provide researchers with information about the importance of RNG-based ciphers and various research techniques for QRNGs that can incorporate quantum-based true randomness in cryptosystems.

Advances and Vulnerabilities in Modern Cryptographic Techniques: A Comprehensive Survey on Cybersecurity in the Domain of Machine/Deep Learning and Quantum Techniques

“…In [71], Hao et al devised an image encryption system utilizing quantum principles, employing a quantum-based PRNG [72] and NEQR model [73] for encryption. However, potential vulnerabilities lie in the PRNG's quantum entanglement basis, risking the security of the generated code stream, and potential threats targeting quantum algorithms might pose a security risk despite their high performance in experiments.…”

Qerberos: A Protocol for Secure Distribution of QRNG Keys

“…TRNGs (True RNGs) exist to amend these vulnerabilities because they extract randomness from the environment, generating higher entropy keys in the process. TRNGs are very diverse [10], and can extract randomness from multiple sources such as signal noise or voltage fluctuation. TRNGs usually need to establish a threshold to determine whether the output should be "0" or "1".…”

“…QRNGs [10]- [12] are a type of TRNG that extract randomness from the behavior of quantum particles, which are intrinsically random by our understanding of the laws of physics. Fig.…”