Lune

CRYPTO2026顶会

Bypassing the Random-Probing Model in Masking Security Proofs

Julien Béguinot, Gianluca Brian, Loïc Masure

2026年份
1被引次数

摘要

Masking, i.e., computing over secret-shared data, is one of the main counter-measures against side-channel analysis, provably secure in the standard noisy-leakage model. However, all the state-of-the-art security proofs rely on a reduction to a more abstract model called random probing (Eurocrypt’14). As a result, the noise requirements of such proofs must scale with the field size of the circuit which, beyond not reflecting real-world physics of target devices, is often prohibitive, especially in the post-quantum era. That is why it is critical to find alternative strategies to the reduction to the random-probing model. In this paper, we establish for the first time a masking security proof bypassing this reduction, answering positively to the above question. Contrary to the common belief that directly working in the noisy-leakage model is not convenient, we show how to reach this goal by leveraging an extension of the Xor lemma. We also show how to leverage the IOS framework (CHES’21) in order to derive composable security directly in the noisy-leakage model. As a result, our bound relies on relaxed noise requirements characterized in a weaker metric than the so far state of the art (Crypto’24, ’19). Moreover, our new proof strategy allows us to derive a security bound for circuits masked with the first-order ISW compiler, for which the optimal noise requirement scales as Θ(∣F∣1/2)\Theta\left(\left|\mathbb{F}\right|^{1/2}\right), whereas proofs using the random-probing model work in a regime of O(∣F∣)\mathcal{O}\left(\left|\mathbb{F}\right|\right). The latter contribution illustrates how some current design choices for masked implementations could be concretely affected: we exhibit two exemplary masked implementations such that the former one is more secure in the (more abstract) random-probing model, whereas the latter one is more secure in the (more realistic) noisy-leakage model.

问问这篇 Paper

智能体会读完全文。

Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。

可以从这些问题问起

智能体调用

Luneget_paper_fulltext

在 Lune 里问

免费开始,无需绑卡

它引用的顶会 Paper18

相关 Paper

黄昏的海面,两侧是细线勾勒的悬崖