Design and Analysis of Reconfigurable Cryptographic Primitives: TRNG and PUF

Kumar, Vikash; Tripathy, Somanath; Mathew, Jimson

doi:10.1007/s41635-021-00120-6

Cited by 4 publications

(1 citation statement)

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“…A memory architecture using multi-bit entropy was used for hardware security applications [32]. The combinational TRNG and PUF techniques were designed for resistive random-access memory and FPGA applications [33,34]. The PUF was used for providing a cryptographic key to optimize the elements in the configurable logic block of the FPGA board [35].…”

Section: Introductionmentioning

confidence: 99%

A Mathematically Generated Noise Technique for Ultrasound Systems

Choi

Shin

2022

Sensors

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Ultrasound systems have been widely used for consultation; however, they are susceptible to cyberattacks. Such ultrasound systems use random bits to protect patient information, which is vital to the stability of information-protecting systems used in ultrasound machines. The stability of the random bit must satisfy its unpredictability. To create a random bit, noise generated in hardware is typically used; however, extracting sufficient noise from systems is challenging when resources are limited. There are various methods for generating noises but most of these studies are based on hardware. Compared with hardware-based methods, software-based methods can be easily accessed by the software developer; therefore, we applied a mathematically generated noise function to generate random bits for ultrasound systems. Herein, we compared the performance of random bits using a newly proposed mathematical function and using the frequency of the central processing unit of the hardware. Random bits are generated using a raw bitmap image measuring 1000 × 663 bytes. The generated random bit analyzes the sampling data in generation time units as time-series data and then verifies the mean, median, and mode. To further apply the random bit in an ultrasound system, the image is randomized by applying exclusive mixing to a 1000 × 663 ultrasound phantom image; subsequently, the comparison and analysis of statistical data processing using hardware noise and the proposed algorithm were provided. The peak signal-to-noise ratio and mean square error of the images are compared to evaluate their quality. As a result of the test, the min entropy estimate (estimated value) was 7.156616/8 bit in the proposed study, which indicated a performance superior to that of GetSystemTime. These results show that the proposed algorithm outperforms the conventional method used in ultrasound systems.

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