Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks
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.oach.
Mon 8 AprDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:30 - 18:00 | |||
16:30 30mTalk | Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks TACAS Link to publication | ||
17:00 30mTalk | Incremental Analysis of Evolving Alloy Models TACAS Wenxi Wang The University of Texas at Austin, Texas, USA, Kaiyuan Wang Google, Inc., Milos Gligoric University of Texas at Austin, Sarfraz Khurshid University of Texas at Austin Link to publication | ||
17:30 30mTalk | Extending a Brainiac Prover to Lambda-Free Higher-Order Logic TACAS Link to publication |