Learning to Localize Leakage of Cryptographic Sensitive Variables

• URL: http://arxiv.org/abs/2503.07464v1
• Date: Mon, 10 Mar 2025 15:42:30 GMT
• Title: Learning to Localize Leakage of Cryptographic Sensitive Variables
• Authors: Jimmy Gammell, Anand Raghunathan, Abolfazl Hashemi, Kaushik Roy,
• Abstract summary: We develop a principled deep learning framework for determining the relative leakage due to measurements recorded at different points in time.<n>This information is invaluable to cryptographic hardware designers for understanding *why* their hardware leaks.
• Score: 13.98875599619791
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While cryptographic algorithms such as the ubiquitous Advanced Encryption Standard (AES) are secure, *physical implementations* of these algorithms in hardware inevitably 'leak' sensitive data such as cryptographic keys. A particularly insidious form of leakage arises from the fact that hardware consumes power and emits radiation in a manner that is statistically associated with the data it processes and the instructions it executes. Supervised deep learning has emerged as a state-of-the-art tool for carrying out *side-channel attacks*, which exploit this leakage by learning to map power/radiation measurements throughout encryption to the sensitive data operated on during that encryption. In this work we develop a principled deep learning framework for determining the relative leakage due to measurements recorded at different points in time, in order to inform *defense* against such attacks. This information is invaluable to cryptographic hardware designers for understanding *why* their hardware leaks and how they can mitigate it (e.g. by indicating the particular sections of code or electronic components which are responsible). Our framework is based on an adversarial game between a family of classifiers trained to estimate the conditional distributions of sensitive data given subsets of measurements, and a budget-constrained noise distribution which probabilistically erases individual measurements to maximize the loss of these classifiers. We demonstrate our method's efficacy and ability to overcome limitations of prior work through extensive experimental comparison with 8 baseline methods using 3 evaluation metrics and 6 publicly-available power/EM trace datasets from AES, ECC and RSA implementations. We provide an open-source PyTorch implementation of these experiments.

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