2019/01/28 by Pengfei Gao, Gao, Pengfei, Hongyi Xie +7
Computer Science · #Advanced Malware Detection Techniques #Cryptographic Implementations and Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Physical Unclonable Functions (PUFs) and Hardware Security
paper · pdf · doi:10.48550/arxiv.1901.09706
openalex publication_date 2019/01/28 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.