Differential Cryptanalysis of Block Ciphers Through the Lens of Symmetry: A Review

Differential cryptanalysis is a fundamental technique in symmetric-key cryptanalysis. While the existing literature and several surveys have separately addressed classical differential attacks, deep learning-assisted cryptanalysis, and quantum-related attacks, a systematic presentation that enables cross-paradigm comparison, lineage mapping, and methodological evaluation is still lacking. To address this gap, this paper organizes its analysis along these three evolutionary threads. First, we trace the evolutionary trajectory of classical differential cryptanalysis. We distill eight representative technical pathways and group them into four categories based on mechanistic characteristics to facilitate cross-comparison. Second, we classify the integration of deep learning with differential cryptanalysis into two distinct paradigms: “deep learning-assisted” and “deep learning-based.” We discuss their roles in feature extraction, trail search, and key-recovery (KR) while also reviewing reproducible evidence, common limitations, and empirical challenges. Third, we survey quantum computing-based approaches. In light of current algorithms and hardware constraints, we examine their potential speedups and applicability boundaries in characteristic search and KR. Our synthesis of existing work reveals distinct capability boundaries for each paradigm and identifies key challenges in their practical application. This paper offers a structured comparative framework, aiming to serve as a reusable reference and baseline for future research.