APT: Efficient Side-Channel Analysis Framework against Inner Product Masking Scheme

Abstract

Due to its provable security and remarkable device-independence, masking has been widely accepted as a good algorithmic-level countermeasure against side-channel attacks. Subsequently, several code-based masking schemes are proposed to strengthen the original Boolean masking (BM) scheme, and Inner Product Masking (IPM) scheme is typically one of those. In this paper, we provide a framework, named analysis with predicted template (APT), for side-channel analysis against the IPM scheme. Following this framework, we propose two attacks based on maximum likelihood and Euclidean distance, respectively. To evaluate their efficiency, we perform simulated experiments on first-order BM and an optimal IPM scheme. The results show that our proposals are equivalent to a second-order CPA against BM scheme, but they are significantly efficient against an optimal IPM. In practical experiments based on an ARM Cortex-M4 architecture, the results of our proposals do not turn out well because of a few outliers in collected leakages. After filtering out these outliers, our proposals perform efficiently as expected. Finally, we argue that the side-channel security of IPM can be improved by keeping the vector L to be randomly selected from an elaborated small set.