Paper 2026/711

Drop-In Masked Modular Reduction for ML-DSA: Cutting Side-Channel Cost in the Root-of-Trust

Reza Azarderakhsh, Florida Atlantic University, PQSecure Technologies

Abstract

Masking is an effective defense against side-channel attacks, yet it remains costly under hardware constraints. The Caliptra Root-of-Trust is a representative case, where its masked ML-DSA implementation incurs about 6× area overhead. We propose a novel first-order masking solution that optimizes Caliptra, achieving significant improvements in area–delay efficiency. Compared to Caliptra’s ML-DSA reduction, our design achieves a 12.1× speedup, reducing LUTs by 86.7% and FFs by 94.5%, while improving area–delay efficiency by 91×. TVLA, with over 1,000,000 traces, shows no first-order leakage, satisfies Caliptra’s security requirements, and significantly improves implementation efficiency.