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Improved parallel mask refreshing algorithms: generic solutions with parametrized non-interference and automated optimizations

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Abstract

Refreshing algorithms are a critical ingredient for secure masking. They are instrumental in enabling sound composability properties for complex circuits, and their randomness requirements dominate the performance overheads in (very) high-order masking. In this paper, we improve a proposal of mask refreshing algorithms from EUROCRYPT 2017 that has excellent implementation properties in software and hardware, in two main directions. First, we provide a generic proof that this algorithm is secure at arbitrary orders—a problem that was left open so far. We introduce parametrized non-interference as a new technical ingredient for this purpose that may be of independent interest. Second, we use automated tools to further explore the design space of such algorithms and provide the best known parallel mask refreshing gadgets for concretely relevant security orders. Incidentally, we also prove the security of a recent proposal of mask refreshing with improved resistance against horizontal attacks from CHES 2017.

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Notes

  1. In our notations, \({{\mathbf {x}}}^{{i}}\) always has to be interpreted modulo \(t+1\), i.e., \({{\mathbf {x}}}^{{i}} \mathrel {\triangleq }{{\mathbf {x}}}^{{i \bmod t+1}}\).

  2. We note that another algorithm can be obtained by producing multiple encodings of 0 via \(\mathsf {ZeroBlock}\) and adding them together after they are produced. This algorithm has a marginally higher memory complexity, and we believe this makes no difference to security—which we leave as a scope for further investigations.

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Acknowledgements

François-Xavier Standaert is a senior research associate of the Belgian Fund for Scientific Research (F.R.S.-FNRS). This work has been funded in parts by the European Union through the ERC project SWORD (724725).

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Barthe, G., Belaïd, S., Dupressoir, F. et al. Improved parallel mask refreshing algorithms: generic solutions with parametrized non-interference and automated optimizations. J Cryptogr Eng 10, 17–26 (2020). https://doi.org/10.1007/s13389-018-00202-2

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