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Boolean-network-based approach for construction of filter generators

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Abstract

In this paper, we view filter generators as Boolean networks (BNs), and discuss their power-analysis-based side-channel analysis. An incompletely specified binary sequence always contains some bits called unnecessary bits comprising 1 or 0. Our motivation for considering this type of sequence is to reduce direct dependencies between side-channel information and key sequences. An algorithm is proposed to determine the unnecessary bits to increase the key search time required for adversaries rather than simply turning all unnecessary bits to 0 (or 1). Then, to reduce area dissipation, under the framework of semi-tensor product (STP) of matrices, the problem of constructing filter generators with minimum number of stages is converted into the one of determining the corresponding transition matrices. Compared with the existing results, the lower bound of the minimum number of stages is provided, which can reduce the exhaustive search time required to find it. Finally, one example is used to illustrate the efficacy of the proposed algorithm.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 61973078), Natural Science Foundation of Jiangsu Province (Grant No. BK20170019), Jiangsu Provincial Key Laboratory of Networked Collective Intelligence (Grant No. BM2017002), Jiangsu Province Six Talent Peaks Project (Grant No. 2015-ZNDW-002), Fundamental Research Funds for the Central Universities (Grant No. 2242019k1G013), and Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grant No. KYCX19_0111).

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Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Bowen Li

  2. School of Mathematics, Southeast University, Nanjing, 210096, China

    Bowen Li & Jianquan Lu

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  1. Bowen Li
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  2. Jianquan Lu
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Correspondence to Jianquan Lu.

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Li, B., Lu, J. Boolean-network-based approach for construction of filter generators. Sci. China Inf. Sci. 63, 212206 (2020). https://doi.org/10.1007/s11432-019-2813-7

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  • DOI: https://doi.org/10.1007/s11432-019-2813-7

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