Nano-Intrinsic True Random Number Generation: A Device to Data Study

Abstract

Recent advances in predictive data analytics and ever growing digitalization and connectivity with explosive expansions in industrial and consumer Internet-of-Things (IoT) has raised significant concerns about security of people's identities and data. It has created close to ideal environment for adversaries in terms of the amount of data that could be used for modeling and also greater accessibility for side-channel analysis of security primitives and random number generators. Random number generators (RNGs) are at the core of most security applications. Therefore, a secure and trustworthy source of randomness is required to be found. Here, we present a differential circuit for harvesting one of the most stochastic phenomenon in solidstate physics, random telegraphic noise (RTN), that is designed to demonstrate significantly lower sensitivities to other sources of noises, radiation and temperature fluctuations. We use RTN in amorphous SrTiO3-based resistive memories to evaluate the proposed true random number generator (TRNG). Successful evaluation on conventional true randomness tests (NIST tests) has been shown. Robustness against using predictive machine learning and side-channel attacks have also been demonstrated in comparison with non-differential readouts methods.

GeSe-Based Ovonic Threshold Switching Volatile True Random Number Generator

“…The LSTM consists of two hidden layers, with 32 neurons in each layer. A prediction rate of 49.63% has been achieved, further supporting that the unpredictability of our TRNG's bit-sequence is acceptable [30].…”

D-RaNGe: Using Commodity DRAM Devices to Generate True Random Numbers with Low Latency and High Throughput

“…A good randomness extraction technique should have two key properties. First, it should have high throughput, i.e., extract as much as randomness possible in a short amount of time [79,135], especially important for applications that require high-throughput random number generation (e.g., security applications [13,15,21,31,37,47,69,80,82,95,101,121,135,142,152,159,162], scienti c simulation [21,95]). Second, it should not disturb the physical process [79,135].…”

“…Cryptography is one typical method for securing systems against various attacks by encrypting the system's data with keys generated with true random values. Many cryptographic algorithms require random values to generate keys in many standard protocols (e.g., TLS/SSL/RSA/VPN keys) to either 1) encrypt network packets, le systems, and data, 2) select internet protocol sequence numbers (TCP), or 3) generate data padding values [31,37,47,69,80,82,152,162]. TRNGs are also commonly used in authentication protocols and in countermeasures against hardware attacks [31], in which psuedo-random number generators (PRNGs) are shown to be insecure [31,152].…”

“…Random number generators (RNGs) are critical components in many di erent applications, including cryptography, scienti c simulation, industrial testing, and recreational entertainment [13,15,31,37,47,69,80,82,95,121,135,142,152,162]. These applications require a mechanism capable of rapidly generating random numbers across a wide variety of operating conditions (e.g., temperature/voltage uctuations, manufacturing variations, malicious external attacks) [158].…”

“…These applications require a mechanism capable of rapidly generating random numbers across a wide variety of operating conditions (e.g., temperature/voltage uctuations, manufacturing variations, malicious external attacks) [158]. In particular, for modern cryptographic applications, a random (i.e., completely unpredictable) number generator is critical to prevent information leakage to a potential adversary [31,37,47,69,79,80,82,152,162].…”