A novel technique to modify the SHOR'S algorithm — Scaling the encryption scheme

Chouhan, Nitesh; Saini, Hemant Kumar; Jain, Sarika

doi:10.1109/icecct.2017.8117822

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The core of Shor algorithm [27,28] is to transform the factorization process of numbers into the period of a function by using some number theories. Multiplication of two large prime numbers is a simple thing, but it is difficult to factorize the product.…”

Section: ) Shor Quantum Search Algorithmmentioning

confidence: 99%

BCsRNG: A Secure Random Number Generator Based on Blockchain

et al. 2022

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Random numbers are widely used in numerical computing, statistical simulation, random sampling, etc. At present, the mechanism for generating random numbers by computers is at risk of being attacked. In some cases, generated random numbers may be predicted. However, current RNGs used in blockchain are not sufficient enough to handle attacks. Therefore, it is necessary to improve the security of random numbers. This paper improves the random number generator and designs a secure random number generator based on blockchain (BCsRNG). In practice, an encapsulated function algorithm of the secure random number is developed with the smart contract. Moreover, an API is provided to facilitate input and output. The experimental results show that the random numbers generated by BCsRNG are difficult to decipher and have higher randomness and security.INDEX TERMS Random number, Blockchain, Smart contract, Security, Random number generator I. INTRODUCTION

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Section: ) Shor Quantum Search Algorithmmentioning

confidence: 99%

BCsRNG: A Secure Random Number Generator Based on Blockchain

et al. 2022

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“…A PRNG involved in a cryptographic algorithm does not add to its system's security if the generator can be reverse engineered in reasonable time. Further, the attacks and methods to reverse engineer the [usually known] deterministic PRNG process often arise after the PRNG is in place, impacting the security of all previous data transported via the system [53]; even our best algorithms [54] possess identified vulnerabilities as future computing methods become available [55]. A variety of reverse-engineering attacks have been developed to target the underlying PRNGs used in various systems.…”

Section: Introductionmentioning

confidence: 99%

Attacks and Defenses for Single-Stage Residue Number System PRNGs

Vennos

George

Michaels

2021

IoT

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This paper explores the security of a single-stage residue number system (RNS) pseudorandom number generator (PRNG), which has previously been shown to provide extremely high-quality outputs when evaluated through available RNG statistical test suites or in using Shannon and single-stage Kolmogorov entropy metrics. In contrast, rather than blindly performing statistical analyses on the outputs of the single-stage RNS PRNG, this paper provides both white box and black box analyses that facilitate reverse engineering of the underlying RNS number generation algorithm to obtain the residues, or equivalently key, of the RNS algorithm. We develop and demonstrate a conditional entropy analysis that permits extraction of the key given a priori knowledge of state transitions as well as reverse engineering of the RNS PRNG algorithm and parameters (but not the key) in problems where the multiplicative RNS characteristic is too large to obtain a priori state transitions. We then discuss multiple defenses and perturbations for the RNS system that fool the original attack algorithm, including deliberate noise injection and code hopping. We present a modification to the algorithm that accounts for deliberate noise, but rapidly increases the search space and complexity. Lastly, we discuss memory requirements and time required for the attacker and defender to maintain these defenses.

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