Revisiting Multiple Ring Oscillator-Based True Random Generators to Achieve Compact Implementations on FPGAs for Cryptographic Applications

Abstract

The generation of random numbers is crucial for practical implementations of cryptographic algorithms. In this sense, hardware security modules (HSMs) include true random number generators (TRNGs) implemented in hardware to achieve good random number generation. In the case of cryptographic algorithms implemented on FPGAs, the hardware implementation of RNGs is limited to the programmable cells in the device. Among the different proposals to obtain sources of entropy and process them to implement TRNGs, those based in ring oscillators (ROs), operating in parallel and combined with XOR gates, present good statistical properties at the cost of high area requirements. In this paper, these TRNGs are revisited, showing a method for area optimization independently of the FPGA technology used. Experimental results show that three ring oscillators requiring only three LUTs are enough to build a TRNG on Artix 7 devices from Xilinx with a throughput of 33.3 Kbps, which passes NIST tests. A throughput of 50 Kbps can be achieved with four ring oscillators, also requiring three LUTs in Artix 7 devices, while 100 Kbps can be achieved using an structure with four ring oscillators requiring seven LUTs.

Privacy-Preserving Techniques in Cloud/Fog and Internet of Things

“…In [1], Parrilla et al proposed a method for area optimization independent of FPGA technology to obtain true random numbers. Most cryptographic algorithms generating a true random number are involved in designing secure cloud/fog and Internet of Things (IoT).…”

A High-Quality Random Number Generator Using Multistage Ring Oscillators and Fast Fourier Transform-Based Noise Extraction

“…In contemporary times, with the escalating computational capabilities accessible to potential attackers, the importance of addressing this issue has become more pronounced. Since the inception of computers, they have served as valuable tools for generating such numbers, initially for statistical and scientific uses [3], and subsequently for cryptographic uses [4]. However, genuinely random numbers cannot be generated through programming alone.…”