LWR-based Quantum-Safe Pseudo-Random Number Generator

Pandit, Anupama Arjun; Kumar, Atul; Mishra, Arun

doi:10.1016/j.jisa.2023.103431

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…3.1.2 Stage 2: Pseudo random number generator (PRNG). Pseudo-Random Number Generator (PRNG) is a computational function that uses a deterministic procedure to produce a series of random integers [34]. Because most cryptographic protocols need the production and use of private values that must be kept hidden from outsiders, PRNG is employed in this cryptographic scheme to generate 100, 000 numbers.…”

Section: Plos Onementioning

confidence: 99%

Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend

Frimpong,

Hayfron Acquah,

Missah

et al. 2024

PLoS ONE

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Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer’s direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm’s mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA’s standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times.

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Section: Plos Onementioning

confidence: 99%

Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend

Frimpong,

Hayfron Acquah,

Missah

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Values between 0 and 1 indicate the level of predictability of a sequence [27]. Now, we calculate H(S) to 100 sequences of period 2 13 , 100 sequences of period 2 14 , 100 sequences of period 2 15 and 100 sequences of period 2 16 generated at random by our PRBG. Despite these sequences do not satisfy H(S) = 1, their entropy is near to 1, as we illustrate in Figure 1, which implies that our PRBG is expected to always generate sequences with a high degree of unpredictability.…”

Section: Entropymentioning

confidence: 99%

“…Fig. 1: Entropy of randomly generated sequences of period 2 13 , 2 14 , 2 15 and 2 16 with our proposed PRBG.…”

Section: Entropymentioning

confidence: 99%

“…Before the existence of test batteries, some PRNGs with poor statistical characteristics were used, for example, the PRNG x 2 mod N [8] and the Middle-square method [9]. Later, much more statistically strong PRNGs emerged, like the congruent linear generators (LCGs) which uses recursion in modular arithmetic [10,11]; generators based on cellular automates that take advantage of the inherent randomness quality of some cellular automata (CA) [12,13]; the linear feedback shift register (LFSR), popular for its easy implementation and efficiency [14]; PRNGs that use lattice-based cryptography, which take advantage of the versatility of this post-quantum cryptographic paradigm and use LFSR to generate sequences [15]; and PRNGs that use chaotic maps, which take advantage of randomness inherent in chaos theory [16].…”

Section: Introductionmentioning

confidence: 99%

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A Pseudorandom Bit Generator Based onBose-type Sidon Sets

Osorio,

Trujullo,

Ruiz

2023

Preprint

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Sidon sets have a variety of applications in mathematics and in real-world problems, such as error-correcting codes, the signal compression in telecommunications, and cryptography. In particular, pseudorandom number (bit) generators with good randomness qualities is critical in the development of secure applications. Based on the construction of Bose-type Sidon sets, in this paper we present the construction of a new pseudorandom bit generator (PRBG) with large period, high degree of entropy and good randomness properties. MSC Classification: 43A46 , 12E20 , 94A55

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“…In doing so, it should be considered that quantum computers not only threaten cryptographic algorithms, but also, the pseudo-random number generators (PRNGs) these algorithms use and depend on. 5 The algorithms being evaluated by NIST fall into two categories: key encapsulation methods (KEMs), and digital signature algorithms (DSAs). The contending algorithms all share a common notion of a seed, which is created, thus far, by PRNGs.…”

Section: Introductionmentioning

confidence: 99%

Post-quantum cryptographic key distribution for autonomous systems operating in contested areas

Jain

Garrett

Cambou

2023

Autonomous Systems: Sensors, Processing and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

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The deployment of post-quantum cryptographic algorithms currently under standardization by NIST have the potential to mitigate quantum computer based attacks, which present a whole new range of cybersecurity challenges. We recognize post-quantum cryptography (PQC) key distribution, across autonomous systems operating in contested areas, as a particularly difficult problem to solve. Verifying the origin and integrity of mission instructions is of extreme importance, especially in the context of autonomous vehicles, missiles, and drones; the lack of which can enable opponents to leverage attacks effecting catastrophic damage to life, property, and strategy.This work proposes a protocol for controlled key distribution through the use of PQC algorithms, strengthened by fingerprints of embedded devices. With this framework, we can assure the origin and correctness of digital information sent to autonomous systems, and enforce that only authorized members can send instructions to these devices. This protocol replaces the deterministic, pseudo-random seed in these PQC protocols with a fingerprint derived from the physical disorder of the embedded device to enhance security and maintain the integrity of the system. We demonstrate how this system can be used to send instructions, verify the origin of the instructions upon receipt, and certify the integrity of the instruction packet received. We also discuss situations in which an attacker attempts to falsify information and how to detect that malicious action. We measure the quality and performance of this prototype system by measuring the latency and bit error rates. We demonstrate this prototype system using the CRYSTALS-DILITHIUM digital signature algorithm (DSA).

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LWR-based Quantum-Safe Pseudo-Random Number Generator

Cited by 7 publications

References 12 publications

Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend

Securing cloud data using secret key 4 optimization algorithm (SK4OA) with a non-linearity run time trend

A Pseudorandom Bit Generator Based onBose-type Sidon Sets

Post-quantum cryptographic key distribution for autonomous systems operating in contested areas

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