Building Correlation Immune Functions from Sets of Mutually Orthogonal Cellular Automata

Luca Mariot, Luca Manzoni

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Abstract

Correlation immune Boolean functions play an important role in the implementation of efficient masking countermeasures for side-channel attacks in cryptography. In this paper, we investigate a method to construct correlation immune functions through families of mutually orthogonal cellular automata (MOCA). First, we show that the orthogonal array (OA) associated to a family of MOCA can be expanded to a binary OA of strength at least 2. To prove this result, we exploit the characterization of MOCA in terms of orthogonal labelings on de Bruijn graphs. Then, we use the resulting binary OA to define the support of a second-order correlation immune function. Next, we perform some computational experiments to construct all such functions up to $n=12$ variables, and observe that their correlation immunity order is actually greater, always at least 3. We conclude by discussing how these results open up interesting perspectives for future research, with respect to the search of new correlation-immune functions and binary orthogonal arrays.
Original languageEnglish
PublisherArXiv.org
Number of pages15
DOIs
Publication statusPublished - 17 Jul 2022

Keywords

  • cs.CR
  • math.CO

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  • Building Correlation Immune Functions from Sets of Mutually Orthogonal Cellular Automata

    Mariot, L. & Manzoni, L., 2023, Cellular Automata and Discrete Complex Systems: 29th IFIP WG 1.5 International Workshop, AUTOMATA 2023, Trieste, Italy, August 30 – September 1, 2023, Proceedings. Manzoni, L., Mariot, L. & Roy Chowdhury, D. (eds.). Cham: Springer, p. 153-164 12 p. (Lecture Notes in Computer Science; vol. 14152).

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