Michal Varchola

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We demonstrate a new high-entropy digital element suitable for True Random Number Generators (TRNGs) embedded in Field Pro-grammable Gate Arrays (FPGAs). The original idea behind this principle lies in the randomness extraction on oscillatory trajectory when a bi-stable circuit is resolving a metastable event. Although such phenomenon is well known in the(More)
I. INTRODUCTION The rapid progress in wireless communication systems, personal communication systems, and wireless sensor networks has brought new opportunities and challenges to be met by engineers and researchers working on the security aspects of new embedded hardware designs. Modern cryptography [1] offers robust solutions to many existing problems in(More)
This paper deals with an evaluation platform for cryptographic True Random Number Generators (TRNGs) based on the hardware implementation of statistical tests for FPGAs. It was developed in order to provide an automatic tool that helps to speed up the TRNG design process and can provide new insights on the TRNG behavior as it will be shown on a particular(More)
—We present and analyze a new method of ran-domness extraction using logic gates only for FPGAs. Random behavior was observed in a ransition Effect Ring Oscillator. This oscillator consists of even number of inverting elements and even number of XOR gates. The first input of XOR is employed in Ring Oscillator chain and the second (control input) is used for(More)
The paper presents hardware SoPC platform for testing performance of various TRNGs embedded in Ac-tel FPGAs. The SoPC was implemented in the recent Ac-tel Fusion ARM enabled FPGA device. It consists of four main blocks-CoreMP7 (Actel's soft-core industry standard ARM7 processor) for managing the SoPC, SRAM and Flash memories embedded inside the FPGA for(More)
We present novel architecture for True Random Number Generator based on Transition Effect Ring Oscillators. The TRNG consists of 16 transition effect oscillators and also malfunction detector. The malfunction detector can evaluate each TRNG bit right after it was generated. The evaluation utilizes described TRNG mathematical model. Our designed True Random(More)