Paper 2026/934

First-Order Masked Fine-Shuffling Implementation Against Side-Channel Attacks with Application to ML-KEM

Abstract

In 2020, Ravi et al. [23] published three shuffling variants with each offering a different performance-security trade-off for protecting the Numeric theoretic Transform (NTT). Among them, the fine-shuffling was proposed as the lightweight variant. The idea is to randomise the order of loading and storing the operands of the butterfly computation using conditional swapping based on random control bit. However, as noted by the authors themselves, basic fine-shuffling implementation suffered from an obvious attack on the conditional swapping mask. So they introduced the bitwise-fine-shuffling to fix this issue. In this paper, we break this implementation using a template attack. The idea is to aggregate the leakage from the 16 bitwise AND operations used to construct the swap mask. The attack has been performed both on simulation and on a STM32F303 target. Then, we propose a masked fine-shuffling variant to protect the fine-shuffling operation. The idea is to mask the secret-dependent memory accesses and apply the swap mask over its boolean sharing instead. The implementation is proven secure against first-order attacks in the probing model. The implementation has been benchmarked on an ARM-Cortex-M4 processor and incurs a total overhead of 25% on the entire ML-KEM768 decapsulation algorithm, compared with 51% overhead for the defeated bitwise-fine-shuffling of[23].