Description
The talk examines power noise modelling through Gaussian Processes for secure True Random Number Generators.
While revisiting one-sided fractional Brownian motion, we obtain novel contributions by quantifying posterior uncertainty in exact analytical form, establishing quasi-stationary properties, and developing rigorous time-frequency analysis. These results are applied to model oscillator fluctuations of power-noise type, enabling closed-form entropy expressions for TRNGs and a novel GPU-accelerated simulation technique valuable for studying non-standard post-processing.
This work bridges machine learning techniques and signal processing to solve hardware security applications.
Keywords
Gaussian Process, Power Noise, True Random Number Generator, Fractional Brownian Motion, Entropy Estimation, Hardware Security, GPU Acceleration
Practical infos
Next sessions
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Adelic reduction of module lattices
Speaker : Henry Bambury - DGA-MI et Inria Rennes
We give a strict generalisation of the LLL algorithm over number fields, based on the reduction theory of $GL(n)$ over the adele ring of a number field. Our algorithm is free of heuristics, with rigorous bounds on output quality and complexity. -- based on joint work with Seungki Kim, Changmin Lee and Phong Nguyen ---
Cryptography
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