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Towards efficient and automated side-channel evaluations at design time

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Abstract

Models and tools developed by the semiconductor community have matured over decades of use. As a result, hardware simulations can yield highly accurate and easily automated pre-silicon estimates for, e.g., timing and area figures. In this work, we design, implement, and evaluate CASCADE, a framework that combines a largely automated full-stack standard cell design flow with the state-of-the-art techniques for side-channel analysis. We show how it can be used to efficiently evaluate side-channel leakage prior to chip manufacturing. Moreover, it is independent of the underlying countermeasure and it can be applied starting from the earliest stages of the design flow. Additionally, we provide experimental validation through assessment of the side-channel security of representative cryptographic circuits. We discuss aspects related to the performance, scalability, and utility to the designers. In particular, we show that CASCADE can evaluate information leakage with 1 million simulated traces in less than 4 h using a single desktop workstation, for a design larger than 100 kGE.

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Notes

  1. A snapshot of CASCADE is available at:

    https://github.com/dsijacic/CASCADE.

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Authors and Affiliations

  1. imec-COSIC, KU Leuven, Belgium

    Danilo Šijačić, Josep Balasch, Bohan Yang & Ingrid Verbauwhede

  2. Intel Labs, Intel Corporation, Hillsboro, OR, USA

    Santosh Ghosh

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  1. Danilo Šijačić
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  2. Josep Balasch
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Correspondence to Danilo Šijačić.

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This work was supported in part by the European Commission through Horizon 2020 research and innovation programmes under the Marie Skłodowska-Curie Grant 643161 and Cathedral ERC Advanced Grant 695305; by the Research Council KU Leuven C1 on Security and Privacy for Cyber-Physical Systems and the Internet of Things with contract number C16/15/058; and by Intel Corporation.

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Šijačić, D., Balasch, J., Yang, B. et al. Towards efficient and automated side-channel evaluations at design time. J Cryptogr Eng 10, 305–319 (2020). https://doi.org/10.1007/s13389-020-00233-8

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