Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

Wallace, Kyle; Moran, Kevin; Novak, Ed; Zhou, Gang; Sun, Kun

doi:10.1109/jiot.2016.2572638

Cited by 35 publications

(25 citation statements)

References 24 publications

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“…[7,4,3] BCH Code Corrector in TRNG. In order to harvest the entropy from the noise source, RNGs in the lightweight environment usually collect sensorbased noise sources such as microphone, accelerometer, magnetometer, gyroscope, temperature, and humidity [20,21]. However, since these noise sources have low entropy [20,21], RNGs have to apply post-processing component so as to reduce biases and to increase the entropy per bit.…”

Section: Experimental Results and Applicationsmentioning

confidence: 99%

A Lightweight BCH Code Corrector of TRNG with Measurable Dependence

Park

Yeom

Kang

2019

Security and Communication Networks

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We propose a new lightweight BCH code corrector of the random number generator such that the bitwise dependence of the output value is controllable. The proposed corrector is applicable to a lightweight environment and the degree of dependence among the output bits of the corrector is adjustable depending on the bias of the input bits. Hitherto, most correctors using a linear code are studied on the direction of reducing the bias among the output bits, where the biased input bits are independent. On the other hand, the output bits of a linear code corrector are inherently not independent even though the input bits are independent. However, there are no results dealing with the independence of the output bits. The well-known von Neumann corrector has an inefficient compression rate and the length of output bits is nondeterministic. Since the heavy cryptographic algorithms are used in the NIST’s conditioning component to reduce the bias of input bits, it is not appropriate in a lightweight environment. Thus we have concentrated on the linear code corrector and obtained the lightweight BCH code corrector with measurable dependence among the output bits as well as the bias. Moreover, we provide some simulations to examine our results.

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Section: Experimental Results and Applicationsmentioning

confidence: 99%

A Lightweight BCH Code Corrector of TRNG with Measurable Dependence

Park

Yeom

Kang

2019

Security and Communication Networks

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“…. , 2 8 − 1} which transform the chaotic streams of GS systems (18) and (19) as well as (20) and (21) into key streams.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…Pseudorandom numbers have wide range of applications in many fields, such as physical systems simulation [3][4][5][6], information encryption [7][8][9][10][11][12][13], entertainment [14,15], and computer simulation [16][17][18][19][20]. In the practical applications, pseudorandom algorithm has almost replaced the stochastic indicator and random number generator based on the hardware.…”

Section: Introductionmentioning

confidence: 99%

Robust Chaos of Cubic Polynomial Discrete Maps with Application to Pseudorandom Number Generators

Han

Lee

Zang

et al. 2019

Mathematical Problems in Engineering

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Based on the robust chaos theorem of S-unimodal maps, this paper studies a kind of cubic polynomial discrete maps (CPDMs) and sets up a novel theorem. This theorem gives general conditions for the occurrence of robust chaos in the CPDMs. By using the theorem, we construct a CPDM. The parameter regions of chaotic robustness of the CPDM are larger than these of Logistic map. By using a fixed point arithmetic, we investigate the cycle lengths of the CPDM and a Logistic map. The results show that the maximum cycle lengths of 1000 chaotic sequences with length 3×107 generated by different initial value conditions exponentially increase with the resolutions. When the resolutions reach 10-7~10-13, the maximum cycle lengths of the cubic polynomial chaotic sequences are significantly greater than these of the Logistic map. When the resolution reaches 10-14, there is the situation without cycle for 1000 cubic polynomial chaotic sequences with length 3×107. By using the CPDM and Logistic map, we design four chaos-based pseudorandom number generators (CPRNGs): CPRNGI, CPRNGII, CPRNGIII, and CPRNGIV. The randomness of two 1000 key streams consisting of 20000 bits is tested, respectively, generated by the four CPRNGs. The result suggests that CPRNGIII based on the cubic polynomial chaotic generalized synchronic system has better performance.

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“…Alternatively, sensors on the wearable and implantable devices can measure physical phenomena of the users, including acceleration, angular velocity, and magnetic field, and such entropy can be harvested to generate truly random numbers [15]. Other researchers have studied the use of sensor-based TRNGs to generate random numbers for mobile, wearable and implantable devices [16,17,18,19,20]. However, there are a few issues that have not been fully addressed.…”

Section: Introductionmentioning

confidence: 99%

Random Number Generation Using Inertial Measurement Unit Signals for On-Body IoT Devices

Sun¹,

Lo²

2018

Living in the Internet of Things: Cybersecurity of the IoT - 2018

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With increasing popularity of wearable and implantable technologies for medical applications, there is a growing concern on the security and data protection of the on-body Internet-of-Things (IoT) devices. As a solution, cryptographic system is often adopted to encrypt the data, and Random Number Generator (RNG) is of vital importance to such system. This paper proposes a new random number generation method for securing on-body IoT devices based on temporal signal variations of the outputs of the Inertial Measurement Units (IMU) worn by the users while walking. As most new wearable and implantable devices have built-in IMUs and walking gait signals can be extracted from these body sensors, this method can be applied and integrated into the cryptographic systems of these new devices. To generate the random numbers, this method divides IMU signals into gait cycles and generates bits by comparing energy differences between the sensor signals in a gait cycle and the averaged IMU signals in multiple gait cycles. The generated bits are then re-indexed in descending order by the absolute values of the associated energy differences to further randomise the data and generate high-entropy random numbers. Two datasets were used in the studies to generate random numbers, where were rigorously tested and passed four well-known randomness test suites, namely NIST-STS, ENT, Dieharder, and RaBiGeTe.

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Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

Cited by 35 publications

References 24 publications

A Lightweight BCH Code Corrector of TRNG with Measurable Dependence

A Lightweight BCH Code Corrector of TRNG with Measurable Dependence

Robust Chaos of Cubic Polynomial Discrete Maps with Application to Pseudorandom Number Generators

Random Number Generation Using Inertial Measurement Unit Signals for On-Body IoT Devices

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