DNA synthesis for true random number generation

Meiser, Linda C.; Koch, Julian; Antkowiak, Philipp L.; Stark, Wendelin J.; Heckel, Reinhard; Grass, Robert N.

doi:10.1038/s41467-020-19757-y

Cited by 31 publications

(26 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A potentially competitive source for true random number generation is automated DNA synthesis, which enables a true random number output of 0.3 MB/s 63 . DNA offers a great independent source of entropy that is air-gapped and orthogonal to other RNG sources.…”

Section: Logic Gates and Circuitsmentioning

confidence: 99%

Synthetic DNA applications in information technology

et al. 2022

Self Cite

View full text Add to dashboard Cite

Synthetic DNA is a growing alternative to electronic-based technologies in fields such as data storage, product tagging, or signal processing. Its value lies in its characteristic attributes, namely Watson-Crick base pairing, array synthesis, sequencing, toehold displacement and polymerase chain reaction (PCR) capabilities. In this review, we provide an overview of the most prevalent applications of synthetic DNA that could shape the future of information technology. We emphasize the reasons why the biomolecule can be a valuable alternative for conventional electronic-based media, and give insights on where the DNA-analog technology stands with respect to its electronic counterparts.

show abstract

Section: Logic Gates and Circuitsmentioning

confidence: 99%

Synthetic DNA applications in information technology

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mechanisms that produce sequences of independently and identically distributed biased bits, abbreviated by i.i.d. hereafter, with partially or unknown bias need to be de-biased such as in Grass et al [7] for instance. Said differently, de-biasing a biased sequence is about extracting the randomness from the aforementioned biased sequence to produce a new unbiased and shorter sequence.…”

Section: Definition Of the Problemmentioning

confidence: 99%

A generalization of the Von Neumann extractor

Gravel

2021

Preprint

View full text Add to dashboard Cite

An iterative randomness extraction algorithm which generalized the Von Neumann's extraction algorithm is detailed, analyzed and implemented in standard C++. Given a sequence of independently and identically distributed biased Bernoulli random variables, to extract randomness from the aforementioned sequence pertains to produce a new sequence of independently and identically distributed unbiased Bernoulli random variables. The iterative construction here is inspired from the work of Stout and Warren [14] who modified appropriately the tree of probabilities produced by recursively repeating the Von Neumann's extraction algorithm. The correctness of the iterative algorithm is proven. The number of biased Bernoulli random variables needed to produce one unbiased instance is the complexity of interest. The complexity depends on the bias of the source. The expected complexity converges toward 3.10220648. . . when the bias tends to 0 and diverges when the bias tends to 1/2. In addition to the expected complexity, some other results that concern the limiting asymptotic construction, and that seem unnoticed in the literature so far, are proven.

show abstract

“…In contrast, physical TRNGs exploit some unpredictable or, at least, difficult to predict physical process and use the outputs to produce a bits sequence that can be truly random [12], thus enabling superior reliability for data encryption and other applications, such as cybersecurity, stochastic modeling, lottery, or games of chance [15][16][17]. Up to date, a series of TRNGs based on different physical sources with different working mechanisms has been investigated to generate considerable random numbers in lieu of conventional pseudo random numbers, such as random telegraph noise (RTN) based on memristors [18][19][20][21][22], thin-film transistor [23][24][25], and triboelectric generator [26,27], laser chaos [28][29][30], photonic integrated chip [31], quantum entropy sources [32][33][34][35], bichromatic laser dye [36], crystallization robot [37], DNA synthesis [38], and so forth. However, majority of aforementioned existing TRNG implementations rely on rigid platforms and expensive complicated manufacturing crafts, which cannot compatibly adapt the portable networked devices and systems since emerging wearable technologies typically demand low-cost and mechanically flexible security hardware components.…”

Section: Introductionmentioning

confidence: 99%

A flexible and stretchable bionic true random number generator

et al. 2022

View full text Add to dashboard Cite

The volume of securely encrypted data transmission increases continuously in modern society with all things connected. Towards this end, true random numbers generated from physical sources are highly required for guaranteeing security of encryption and decryption schemes for exchanging sensitive information. However, majority of true random number generators (TRNGs) are mechanically rigid, and thus cannot be compatibly integrated with some specific flexible platforms. Herein, we present a flexible and stretchable bionic TRNG inspired by the uniqueness and randomness of biological architectures. The flexible TRNG film is molded from the surface microstructures of natural plants (e.g., ginkgo leaf) via a simple, low-cost, and environmentally friendly manufacturing process. In our proof-of-principle experiment, the TRNG exhibits a fast generation speed of up to 1.04 Gbit/s, in which random numbers are fully extracted from laser speckle patterns with a high extraction rate of 72%. Significantly, the resulting random bit streams successfully pass all randomness test suites including NIST, TestU01, and DIEHARDER. Even after 10,000 times cyclic stretching or bending tests, or during temperature shock (−25–80 °C), the bionic TRNG still reveals robust mechanical reliability and thermal stability. Such a flexible TRNG shows a promising potential in information security of emerging flexible networked electronics. Electronic Supplementary Material Supplementary material (light path diagram of transmitted laser speckle, pseudo random pattern, statistical distribution of bionic microstructures, haze of the bionic TRNG film, multi-layer circular laser intensity pattern, percentage of bit 0/1 for different hashed images, Pearson correlation coefficient between 100 different speckle images, the whole process of randomness extraction, SEM images of the flexible TRNG film after 10,000 times stretching and bending, continuous work stability of the TRNG at low or high temperature, light path diagram of reflective laser speckle, and detailed randomness test results of NIST, TestU01, and DIEHARDER) is available in the online version of this article at 10.1007/s12274-022-4109-9.

show abstract

DNA synthesis for true random number generation

Cited by 31 publications

References 42 publications

Synthetic DNA applications in information technology

Synthetic DNA applications in information technology

A generalization of the Von Neumann extractor

A flexible and stretchable bionic true random number generator

Contact Info

Product

Resources

About