Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks

TL;DR

This paper presents a hybrid verification method combining type inference and model-counting to assess the security of masked arithmetic programs against power side-channel attacks, with a tool implementation and empirical evaluation.

Contribution

It introduces a novel hybrid approach for verifying masked programs' security, integrating type inference for efficiency and model-counting for completeness.

Findings

Effective verification of masked programs against side-channel attacks

Quantification of security levels when masking is imperfect

Demonstrated efficiency and effectiveness on cryptographic benchmarks

Abstract

Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for completeness. In case that the program is not perfectly masked, we also provide a method to quantify the security level of the program. We implement our methods in a tool QMVerif and evaluate it on cryptographic benchmarks. The experimental results show the effectiveness and efficiency of our approach.