Formal Verification of Masked Hardware Implementations in the Presence of Glitches

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Abstract

Masking provides a high level of resistance against side-channel analysis. However, in practice there are many possible pitfalls when masking schemes are applied, and implementation flaws are easily overlooked. Over the recent years, the formal verification of masked software implementations has made substantial progress. In contrast to software implementations, hardware implementations are inherently susceptible to glitches. Therefore, the same methods tailored for software implementations are not readily applicable. In this work, we introduce a method to formally verify the security of masked hardware implementations that takes glitches into account. Our approach does not require any intermediate modeling steps of the targeted implementation. The verification is performed directly on the circuit’s netlist in the probing model with glitches and covers also higher-order flaws. For this purpose, a sound but conservative estimation of the Fourier coefficients of each gate in the netlist is calculated, which characterize statistical dependence of the gates on the inputs and thus allow to predict possible leakages. In contrast to existing practical evaluations, like t-tests, this formal verification approach makes security statements beyond specific measurement methods, the number of evaluated leakage traces, and the evaluated devices. Furthermore, flaws detected by the verifier are automatically localized. We have implemented our method on the basis of a SAT solver and demonstrate the suitability on a range of correctly and incorrectly protected circuits of different masking schemes and for different protection orders. Our verifier is efficient enough to prove the security of a full masked first-order AES S-box, and of the Keccak S-box up to the third protection order.

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APA

Bloem, R., Gross, H., Iusupov, R., Könighofer, B., Mangard, S., & Winter, J. (2018). Formal Verification of Masked Hardware Implementations in the Presence of Glitches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10821 LNCS, pp. 321–353). Springer Verlag. https://doi.org/10.1007/978-3-319-78375-8_11

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