Nano-Intrinsic True Random Number Generation

Jeeson Kim; Taimur Ahmed; Hussein Nili; Nhan Duy Truong; Jiawei Yang; Doo Seok Jeong; Sharath Sriram; Damith C. Ranasinghe; Omid Kavehei

arXiv · 1701.06020

Nano-Intrinsic True Random Number Generation

Jeeson Kim·Taimur Ahmed·Hussein Nili·Nhan Duy Truong·Jiawei Yang·Doo Seok Jeong·Sharath Sriram·Damith C. Ranasinghe·Omid Kavehei

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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 solid-state 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.

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Jeeson Kim, Taimur Ahmed, Hussein Nili, Nhan Duy Truong, Jiawei Yang, Doo Seok Jeong, Sharath Sriram, Damith C. Ranasinghe, Omid Kavehei. 2017-01-21. Nano-Intrinsic True Random Number Generation. https://arxiv.org/abs/1701.06020

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