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Families of Pseudorandom Binary Sequences with Low Cross-Correlation Measure

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Cryptography and Information Security in the Balkans (BalkanCryptSec 2014)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 9024))

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Abstract

Pseudorandom sequences are used in many areas of cryptography for instance as a key stream of stream ciphers. These sequences have to be unpredictable and resist to known attacks, hence they are supposed to satisfy some properties e.g., large linear complexity and low correlation. Since they are generated by a sequence generator, we also need to talk about a family of sequences and its properties. A family of sequences must have complex and rich structure e.g., large family size, large family complexity, strict avalanche property and low cross-correlation. In this study, we present two large families of pseudorandom binary sequences with low cross-correlation measure. In fact, we extend the family construction method given by K. Gyarmati, C. Mauduit and A. Sárközy and obtain larger families.

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Acknowledgment

This paper was written when the author was visiting Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz. The hospitality of RICAM is gratefully acknowledged. The author has benefited from discussions with Arne Winterhof. The visit was supported by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under Grant No. 2219. The author thanks anonymous referees for useful suggestions.

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Authors and Affiliations

  1. Department of Mathematics, Hacettepe University, Beytepe Campus, Ankara, Turkey

    Oğuz Yayla

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  1. Oğuz Yayla
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Correspondence to Oğuz Yayla .

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Editors and Affiliations

  1. Istanbul Technical University, Istanbul, Turkey

    Berna Ors

  2. Katholieke Universiteit Leuven, Leuven, Belgium

    Bart Preneel

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Yayla, O. (2015). Families of Pseudorandom Binary Sequences with Low Cross-Correlation Measure. In: Ors, B., Preneel, B. (eds) Cryptography and Information Security in the Balkans. BalkanCryptSec 2014. Lecture Notes in Computer Science(), vol 9024. Springer, Cham. https://doi.org/10.1007/978-3-319-21356-9_3

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  • DOI: https://doi.org/10.1007/978-3-319-21356-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21355-2

  • Online ISBN: 978-3-319-21356-9

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