Abstract

In this paper we present a novel true random number generator based on high-precision edge sampling. We use two novel techniques to increase the throughput and reduce the area of the proposed randomness source: variable-precision phase encoding and repetitive sampling. The first technique consists of encoding the oscillator phase with high precision in the regions around the signal edges and with low precision everywhere else. This technique results in a compact implementation at the expense of reduced entropy in some samples. The second technique consists of repeating the sampling at high frequency until the phase region encoded with high precision is captured. This technique ensures that only the high-entropy bits are sent to the output. The combination of the two proposed techniques results in a secure TRNG, which suits both ASIC and FPGA implementations. The core part of the proposed generator is implemented with 10 look-up tables (LUTs) and 5 flip-flops (FFs) of a Xilinx Spartan-6 FPGA, and achieves a throughput of 1.15 Mbps with 0.997 bits of Shannon entropy. On Intel Cyclone V FPGAs, this implementation uses 10 LUTs and 6 FFs, and achieves a throughput of 1.07 Mbps. This TRNG design is supported by a stochastic model and a formal security evaluation.

Highlights

  • True Random Number Generators (TRNGs) are essential building blocks of modern embedded security systems

  • The second column of the table indicates the availability of stochastic models for each TRNG design

  • Among Xilinx TRNG designs listed in the table, ES-TRNG achieves the smallest hardware footprint

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Summary

Introduction

True Random Number Generators (TRNGs) are essential building blocks of modern embedded security systems. They enable various cryptographic algorithms, protocols and secured implementations by providing secret keys, initialization vectors, random challenges and masks. The security of these applications relies on the uniformity and unpredictability of the utilized random numbers. True randomness cannot be obtained via computational methods Instead, physical phenomena such as noise in electronic devices should be the source of the unpredictable nature of TRNGs. Instead, physical phenomena such as noise in electronic devices should be the source of the unpredictable nature of TRNGs Due to their importance for security, TRNGs are subjected to strict evaluations in the process of industrial certification. IACR Transactions on Cryptographic Hardware and Embedded Systems ISSN 2569-2925, Vol 2018, No 3, pp. 267–292 DOI:10.13154/tches.v2018.i3.267-292

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