Helper Data Algorithms

Helper Data Algorithms is a methodological framework in the field of physical unclonable functions that generates stable, reproducible cryptographic keys from noisy hardware-intrinsic physical characteristics by employing error correction and privacy-preserving techniques to mitigate intra-device variation while securing against adversarial reconstruction.

22

Publications

1.8K

Citations

56

Authors

27

Institutions

Publications per year

2017–2026

4

Variants

Helper Data Schemes

Authors

56

  1. IV
    Ingrid Verbauwhede

    KU Leuven, Belgium

    5 publications · 531 citations

    5

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  2. JD
    Jeroen Delvaux

    KU Leuven, Belgium

    4 publications · 355 citations

    4

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  3. TK
    T.A.M. Kevenaar

    Philips (Netherlands), Netherlands

    2 publications · 163 citations

    2

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  4. CB
    Christoph Busch

    Darmstadt University of Applied Sciences, Germany

    2 publications · 30 citations

    2

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  5. DM
    Daniel M. Hartung

    Oregon State University, United States

    2 publications · 30 citations

    2

    h-index

1–5 of 56

Venues

  1. 2

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  2. 2

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  3. 1

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  4. 1

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  5. Electronics

    1 publications · 27 citations

    1

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1–5