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Cryptographic triboelectric random number generator with gentle breezes of an entropy source.

ABSTRACT: A wind-driven triboelectric nanogenerator (W-TENG) is a promising energy harvesting device due to its clean, ubiquitous and unexhausted properties. In addition, a W-TENG induces unpredictable chaotic outputs from wind flow that can serve as an entropy source for cryptography. This can be applied to a true random number generator (TRNG) for a secured system due to its inherent turbulent nature; thus, a W-TENG with a two-in-one structure can simultaneously generate both power and true random numbers. However, a previously reported W-TENG had one major drawback: a wind velocity of 10 m/s is required for stable energy harvesting by wind force. Thus, it is timely to demonstrate a W-TENG-based RNG whose operating condition is below 3 m/s, which is a gentle breeze similar to natural wind. In this study, we demonstrate a wind-driven cryptographic triboelectric random number generator (WCT-RNG) by using a W-TENG whose operating condition for wind speed is below 3 m/s by adopting a rear-fixed film structure instead of a conventional structure. The rear-fixed film refers to the fluttering film being freestanding on the front-side and fixed on the rear-side, where the front- and rear-sides are the wind inlet and outlet, respectively. The WCT-RNG enables the W-TENG to operate below a 3 m/s wind velocity. Because of this, the working time of the WCT-RNG is dramatically enhanced from only 8-42% at an average altitude above sea level. As the capability of operating at low wind speeds is significantly improved, a WCT-RNG becomes more useful and practical for generating both power and true random numbers in a single device. The device can thereby lead to the construction of a self-powered TRNG and secure communication for Internet of Things (IoT) devices in various environments, even under a gentle breeze. In this study, we explain the design of a WCT-RNG structure and also evaluate its randomness by using an NIST SP 800-22 B test suite with a reliability test.

SUBMITTER: Kim MS

PROVIDER: S-EPMC10791744 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Cryptographic triboelectric random number generator with gentle breezes of an entropy source.

Kim Moon-Seok MS Tcho Il-Woong IW Choi Yang-Kyu YK

Scientific reports 20240116 1

A wind-driven triboelectric nanogenerator (W-TENG) is a promising energy harvesting device due to its clean, ubiquitous and unexhausted properties. In addition, a W-TENG induces unpredictable chaotic outputs from wind flow that can serve as an entropy source for cryptography. This can be applied to a true random number generator (TRNG) for a secured system due to its inherent turbulent nature; thus, a W-TENG with a two-in-one structure can simultaneously generate both power and true random numbe ...[more]

PMID: 38228787

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Project description: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 materialSupplementary 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.

Project description:Pseudo-random number generators (PRNGs) are software algorithms generating a sequence of numbers approximating the properties of random numbers. They are critical components in many information systems that require unpredictable and nonarbitrary behaviors, such as parameter configuration in machine learning, gaming, cryptography, and simulation. A PRNG is commonly validated through a statistical test suite, such as NIST SP 800-22rev1a (NIST test suite), to evaluate its robustness and the randomness of the numbers. In this paper, we propose a Wasserstein distance-based generative adversarial network (WGAN) approach to generating PRNGs that fully satisfy the NIST test suite. In this approach, the existing Mersenne Twister (MT) PRNG is learned without implementing any mathematical programming code. We remove the dropout layers from the conventional WGAN network to learn random numbers distributed in the entire feature space because the nearly infinite amount of data can suppress the overfitting problems that occur without dropout layers. We conduct experimental studies to evaluate our learned pseudo-random number generator (LPRNG) by adopting cosine-function-based numbers with poor random number properties according to the NIST test suite as seed numbers. The experimental results show that our LPRNG successfully converted the sequence of seed numbers to random numbers that fully satisfy the NIST test suite. This study opens the way for the "democratization" of PRNGs through the end-to-end learning of conventional PRNGs, which means that PRNGs can be generated without deep mathematical know-how. Such tailor-made PRNGs will effectively enhance the unpredictability and nonarbitrariness of a wide range of information systems, even if the seed numbers can be revealed by reverse engineering. The experimental results also show that overfitting was observed after about 450,000 trials of learning, suggesting that there is an upper limit to the number of learning counts for a fixed-size neural network, even when learning with unlimited data.

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Cryptographic triboelectric random number generator with gentle breezes of an entropy source.

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Cryptographic triboelectric random number generator with gentle breezes of an entropy source.

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