Practical True Random Number Generator Using CMOS Image Sensor Dark Noise

Park, Byung Kwon; Park, Hojoong; Kim, Yong‐Su; Kang, Ju-Sung; Yeom, Yongjin; Ye, Changhui; Moon, Sung; Han, Sang-Wook

doi:10.1109/access.2019.2926825

Cited by 23 publications

(15 citation statements)

References 32 publications

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“…On the one hand, we assess the quality of the Gait data as a good source of entropy using the NIST SP 800-90B recommendation [35], [36]. On the other hand, and after post-processing, we analyze the randomness quality of the proposed TRNG using well-established randomness battery of tests like DIEHARDER [37] or NIST 800-22 [38], [39]. Figure 3 sum ups the process and outlines the test suites used and each step.…”

Section: Resultsmentioning

confidence: 99%

A True Random Number Generator Based on Gait Data for the Internet of You

et al. 2020

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The Internet of Things (IoT) is more and more a reality, and every day the number of connected objects increases. The growth is practically exponential-there are currently about 8 billion and expected to reach 21 billion in 2025. The applications of these devices are very diverse and range from home automation, through traffic monitoring or pollution, to sensors to monitor our health or improve our performance. While the potential of their applications seems to be unlimited, the cyber-security of these devices and their communications is critical for a flourishing deployment. Random Number Generators (RNGs) are essential to many security tasks such as seeds for key-generation or nonces used in authentication protocols. Till now, True Random Number Generators (TRNGs) are mainly based on physical phenomena, but there is a new trend that uses signals from our body (e.g., electrocardiograms) as an entropy source. Inspired by the last wave, we propose a new TRNG based on gait data (six 3-axis gyroscopes and accelerometers sensors over the subjects). We test both the quality of the entropic source (NIST SP800-90B) and the quality of the random bits generated (ENT, DIEHARDER and NIST 800-22). From this in-depth analysis, we can conclude that: 1) the gait data is a good source of entropy for random bit generation; 2) our proposed TRNG outputs bits that behave like a random variable. All this confirms the feasibility and the excellent properties of the proposed generator. INDEX TERMS True random number generator, Internet of Things, gait data, entropy, randomness.

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Section: Resultsmentioning

confidence: 99%

A True Random Number Generator Based on Gait Data for the Internet of You

et al. 2020

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“…Generating random numbers using the optical heterodyne of two chaotic optical-feedback semiconductor lasers as entropy source is also reported [31]. The dark current noise in active-pixels in CMOS image sensors can also be exploited for generating true random numbers [32]. These implementations provide high performance and strong randomness due to the quantum nature of the entropy source.…”

Section: Related Workmentioning

confidence: 99%

“…In the literature, using optical components as quantum entropy source for a TRNG is commonly reported [22,[29][30][31][32][33]. For example, an intradyne receiver is utilized to achieve wide noise bandwidth of 11GHz [22].…”

Section: Related Workmentioning

confidence: 99%

Cryptographically strong random number generation using integrated CMOS photodiodes for low-cost microcontroller based applications

2021

Turk J Elec Eng & Comp Sci

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In this work, we propose a method to generate random numbers for low-cost, low-power, resource-limited low data-rate microcontrollers using integrated CMOS photodiodes. The proposed method utilizes an integrated CMOS photodiode in the photovoltaic mode as the entropy source. The method is based on serially capturing analog values derived from the integrated CMOS photodiode. The entropy of these values increased by a custom algorithm. The proposed random number generator is devised using an integrated CMOS photodiode manufactured in 180 nm standard CMOS technology. The wide applicably of the random number generator is demonstrated by realizing it on a lowcost Arduino UNO board placed in a typical room environment. The implemented random number generator passes NIST-SP800-22 and AIS31 randomness tests at high scores. The proposed method achieved 5.4Kbps throughput and 7.2% total significance level without any post-processing. The test results show the high cryptographical strength of the proposed method makes it a promising alternative to the currently used random number generation algorithms in low-cost low-resources low-data rate microcontroller-based applications.

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“…Random number generators are critical components that are responsible of generating public keys, private keys, and other kinds of random numbers that are utilized in cryptographic applications and data security [1]- [5]. The explosive growth of Internet-of-Things (IoTs) devices places new challenges on random number generators including energy efficiency, hardware security, and flexibility.…”

Section: Introductionmentioning

confidence: 99%

A Low Power Circuit Design for Chaos-Key Based Data Encryption

et al. 2020

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Dynamic and non-linear systems have been used to generate random bits in high-security applications for decades. In this perspective, due to their stochastic characteristic, chaotic systems have been emerging as the natural choice for the generation of random bits. This paper presents the design and the implementation of a chaos-based true random number generator and a chaos-key based data encryption scheme for secure communications. The mathematical expression of the dynamic system is presented and analyzed to evaluate the possibility of chaos occurrence. Then, the chaotic system is realized at the circuit level using 130 nm CMOS technology to generate random bit sequences, which are utilized in data encryption. Chaotic signal outputs of the chaos-based random number generator circuit are sampled at a maximum frequency of 50 MHz, enabling a high throughput of random bits. The core of the chaotic circuit consumes 630µW in static mode and a maximum of 660µW in running mode. The chaos-based one-time pad encryption scheme using the chaos-key generator shows the advantages of using this random number generator in secure communications. In this context, the data secrecy is compared to the advanced encryption standard AES128. Moreover, the design is simulated in different working conditions such as voltage supply and temperature variations, where it is shown that the random bit output benefits from a high entropy per bit and passes the standard statistical test suite (NIST) for cryptographic applications. INDEX TERMS Chaos, random key, security, CMOS, true random number generator, one-time pad encryption, image encryption, and NIST.

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Practical True Random Number Generator Using CMOS Image Sensor Dark Noise

Cited by 23 publications

References 32 publications

A True Random Number Generator Based on Gait Data for the Internet of You

A True Random Number Generator Based on Gait Data for the Internet of You

Cryptographically strong random number generation using integrated CMOS photodiodes for low-cost microcontroller based applications

A Low Power Circuit Design for Chaos-Key Based Data Encryption

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