Skip to main content

Look into the Mirror: Evolving Self-dual Bent Boolean Functions

  • Conference paper
  • First Online:
Genetic Programming (EuroGP 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14631))

Included in the following conference series:

  • 116 Accesses

Abstract

Bent Boolean functions are important objects in cryptography and coding theory, and there are several general approaches for constructing such functions. Metaheuristics proved to be a strong choice as they can provide many bent functions, even when the size of the Boolean function is large (e.g., more than 20 inputs). While bent Boolean functions represent only a small part of all Boolean functions, there are several subclasses of bent functions providing specific properties and challenges. One of the more interesting subclasses comprises (anti-)self-dual bent Boolean functions.

This paper provides a detailed experimentation with evolutionary algorithms with the goal of evolving (anti-)self-dual bent Boolean functions. We experiment with two encodings and two fitness functions to evolve self-dual bent Boolean functions. Our experiments consider Boolean functions with sizes of up to 16 inputs, and we successfully construct self-dual bent functions for each dimension. Moreover, we notice that the difficulty of evolving self-dual bent functions is similar to evolving bent Boolean functions, despite self-dual bent functions being much rarer.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Notes

  1. 1.

    The function AND2 behaves the same as the function AND but with the second input inverted.

  2. 2.

    Evolutionary Computation Framework, http://solve.fer.hr/ECF/.

References

  1. Adams, C.: The CAST-128 Encryption Algorithm. RFC 2144, May 1997. https://doi.org/10.17487/RFC2144, https://www.rfc-editor.org/info/rfc2144

  2. Carlet, C.: Boolean Functions for Cryptography and Coding Theory. Cambridge University Press, Cambridge (2021). https://doi.org/10.1017/9781108606806

  3. Carlet, C., Mesnager, S.: Four decades of research on bent functions. Des. Codes Cryptogr. 78(1), 5–50 (2016)

    Article  MathSciNet  Google Scholar 

  4. Dillon, J.F.: Elementary Hadamard difference sets. Ph.D. thesis, Univ. of Maryland (1974)

    Google Scholar 

  5. Djurasevic, M., Jakobovic, D., Mariot, L., Picek, S.: A survey of metaheuristic algorithms for the design of cryptographic Boolean functions. Cryptogr. Commun. 15(6), 1171–1197 (2023). https://doi.org/10.1007/s12095-023-00662-2

  6. Dobbertin, H.: Construction of bent functions and balanced Boolean functions with high nonlinearity. In: Preneel, B. (ed.) FSE 1994. LNCS, vol. 1008, pp. 61–74. Springer, Heidelberg (1995). https://doi.org/10.1007/3-540-60590-8_5

    Chapter  Google Scholar 

  7. Hrbacek, R., Dvorak, V.: Bent function synthesis by means of cartesian genetic programming. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 414–423. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_41

    Chapter  Google Scholar 

  8. Husa, J., Dobai, R.: Designing bent Boolean functions with parallelized linear genetic programming. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1825–1832. GECCO ’17, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3067695.3084220

  9. Jakobovic, D., Picek, S., Martins, M.S., Wagner, M.: Toward more efficient heuristic construction of Boolean functions. Appl. Soft Comput. 107, 107327 (2021). https://doi.org/10.1016/j.asoc.2021.107327, https://www.sciencedirect.com/science/article/pii/S1568494621002507

  10. Kerdock, A.: A class of low-rate nonlinear binary codes. Inf. Control 20(2), 182–187 (1972)

    Article  MathSciNet  Google Scholar 

  11. MacWilliams, F.J., Sloane, N.J.A.: The Theory of Error-Correcting Codes. Elsevier, Amsterdam, North Holland (1977). ISBN: 978-0-444-85193-2

    Google Scholar 

  12. Mariot, L., Jakobovic, D., Leporati, A., Picek, S.: Hyper-bent Boolean functions and evolutionary algorithms. In: Sekanina, L., Hu, T., Lourenço, N., Richter, H., García-Sánchez, P. (eds.) EuroGP 2019. LNCS, vol. 11451, pp. 262–277. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16670-0_17

    Chapter  Google Scholar 

  13. Mariot, L., Leporati, A.: A genetic algorithm for evolving plateaued cryptographic Boolean functions. In: Dediu, A.-H., Magdalena, L., Martín-Vide, C. (eds.) TPNC 2015. LNCS, vol. 9477, pp. 33–45. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26841-5_3

    Chapter  Google Scholar 

  14. Mariot, L., Saletta, M., Leporati, A., Manzoni, L.: Heuristic search of (semi-)bent functions based on cellular automata. Nat. Comput. 21(3), 377–391 (2022)

    Article  MathSciNet  Google Scholar 

  15. McFarland, R.L.: A family of difference sets in non-cyclic groups. J. Comb. Theory Ser. A 15(1), 1–10 (1973). https://doi.org/10.1016/0097-3165(73)90031-9, https://www.sciencedirect.com/science/article/pii/0097316573900319

  16. Mesnager, S.: Bent Functions. Springer International Publishing, Cham (2016). https://doi.org/10.1007/978-3-319-32595-8

  17. Mesnager, S.: Linear codes from functions. In: Huffman, W.C., Solé, J.L.K.P. (eds.) A Concise Encyclopedia of Coding Theory. p. 94 pages in Chapter 20. Press/Taylor and Francis Group (2021)

    Google Scholar 

  18. Miller, J.F.: An empirical study of the efficiency of learning Boolean functions using a cartesian genetic programming approach. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 2, pp. 1135–1142. GECCO’99, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1999)

    Google Scholar 

  19. Olsen, J., Scholtz, R., Welch, L.: Bent-function sequences. IEEE Trans. Inf. Theory 28(6), 858–864 (1982)

    Article  MathSciNet  Google Scholar 

  20. Picek, S., Jakobovic, D.: Evolving algebraic constructions for designing bent Boolean functions. In: Proceedings of the Genetic and Evolutionary Computation Conference 2016, pp. 781–788. GECCO ’16, Association for Computing Machinery, New York, NY, USA (2016). https://doi.org/10.1145/2908812.2908915

  21. Picek, S., Jakobovic, D.: Evolutionary computation and machine learning in security. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1572–1601. GECCO ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3520304.3534087

  22. Picek, S., Jakobovic, D., O’Reilly, U.M.: Cryptobench: benchmarking evolutionary algorithms with cryptographic problems. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, pp. 1597–1604. GECCO ’17, Association for Computing Machinery, New York, NY, USA (2017). https://doi.org/10.1145/3067695.3082535

  23. Picek, S., Knezevic, K., Mariot, L., Jakobovic, D., Leporati, A.: Evolving bent quaternary functions. In: 2018 IEEE Congress on Evolutionary Computation, CEC 2018, Rio de Janeiro, Brazil, 8–13 July 2018, pp. 1–8. IEEE (2018)

    Google Scholar 

  24. Picek, S., Sisejkovic, D., Jakobovic, D.: Immunological algorithms paradigm for construction of Boolean functions with good cryptographic properties. Eng. Appl. Artif. Intell. 62, 320–330 (2017). https://doi.org/10.1016/j.engappai.2016.11.002, http://www.sciencedirect.com/science/article/pii/S0952197616302044

  25. Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming. Lulu Enterprises Ltd., UK (2008)

    Google Scholar 

  26. Rothaus, O.: On bent functions. J. Comb. Theory Ser. A 20(3), 300–305 (1976)

    Google Scholar 

  27. Yan, L., et al.: IGA: an improved genetic algorithm to construct weightwise (almost) perfectly balanced Boolean functions with high weightwise nonlinearity. In: Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security, pp. 638–648. ASIA CCS ’23, Association for Computing Machinery, New York, NY, USA (2023). https://doi.org/10.1145/3579856.3590337

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Domagoj Jakobovic .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Carlet, C., Durasevic, M., Jakobovic, D., Mariot, L., Picek, S. (2024). Look into the Mirror: Evolving Self-dual Bent Boolean Functions. In: Giacobini, M., Xue, B., Manzoni, L. (eds) Genetic Programming. EuroGP 2024. Lecture Notes in Computer Science, vol 14631. Springer, Cham. https://doi.org/10.1007/978-3-031-56957-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56957-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56956-2

  • Online ISBN: 978-3-031-56957-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics