Predictably random

Deborah J. Bennett, Stephen G. Gasiorowicz

Jiaxin Yu, Shaofei Wang, Xin Xue +8 more

A decorrelation preprocessing method based on linear predictive coding (LPC) is proposed for enhancing entropy-source signals from a Brillouin random fiber laser (BRFL). Unlike structure-optimization-based approaches, the proposed algorithm directly suppresses predictable components in raw BRFL entropy-source signals by extracting prediction residuals from historical-sample-based linear prediction. Experimental results show that LPC preprocessing significantly reduces the autocorrelation sidelobe ratio of the BRFL sampling sequence from −0.044 to −28.917dB, increases the spectral flatness from 0.005 to 0.952, and improves the amplitude min-entropy from 5.454 to 6.190 bit/sample. After random-bit postprocessing, the generated bitstream exhibits good 0/1 balance and low residual correlation and successfully passes the NIST randomness tests. These results demonstrate the effectiveness of LPC-based decorrelation preprocessing in enhancing BRFL entropy-source signals, indicating its potential for fiber-laser-based physical random number generation and secure information processing.

Hugo Roussille, Lionel Djadaojee, Frédéric Chevy

Cryptography techniques rely on chains of random numbers used to generate safe encryption keys. Since random number generator algorithms are in fact pseudo-random their behavior can be predicted if the generation method is known and as such they cannot be used for perfectly safe communications. In this article, we present a perfectly random generator based on quantum measurement processes. The main advantage of such a generator is that using quantum mechanics, its behavior cannot be predicted in any way. We verify the randomness of our generator and compare it to commonly used pseudo-random generators.

Federico Levi

Ali Abdolrahimi Zarnagh, Ali Motazedifard

Generating reliable random and pseudo-random sequences is important in many electronic and signal processing systems, such as secure communications, radar, spread-spectrum methods, and autonomous platforms. Although true and quantum random number generators provide stronger unpredictability, classical pseudo-random number generators, including Linear Feedback Shift Registers (LFSRs) and the Mersenne Twister (MT), are still widely used because they are efficient and easy to implement. This work introduces a user-friendly software platform for generating, analyzing, and evaluating the predictability of pseudo-random bit sequences. The software supports two main functions: generating sequences using classical PRNGs and hybrid combinations, and analyzing input sequences through statistical measures and data-driven methods. In particular, hybrid LFSR-MT structures are studied to examine how they affect sequence complexity and resistance to prediction. The platform also includes machine-learning and deep-learning tools to investigate when deterministic PRNGs may remain partially predictable, even when their structure becomes more complex. The results show that algorithmic random sequence generators have inherent limitations in terms of unpredictability, which supports the use of quantum random sequences in security-critical applications. A comparative study between classical LFSR-MT sequences and quantum random sequences shows that quantum randomness offers higher unpredictability due to its non-deterministic physical origin. The potential use of quantum random sequences in jamming applications is also discussed, highlighting their improved robustness against prediction-based attacks. Overall, the proposed software provides a practical tool for analyzing, comparing, and benchmarking random sequence generators in modern electronic, sensing, and quantum-enabled communication systems.

Daniela Frauchiger, Renato Renner

Randomness is crucial for a variety of applications, ranging from gambling to computer simulations, and from cryptography to statistics. However, many of the currently used methods for generating randomness do not meet the criteria that are necessary for these applications to work properly and safely. A common problem is that a sequence of numbers may look random but nevertheless not be truly random. In fact, the sequence may pass all standard statistical tests and yet be perfectly predictable. This renders it useless for many applications. For example, in cryptography, the predictability of a randomly" chosen password is obviously undesirable. Here, we review a recently developed approach to generating true | and hence unpredictable | randomness.