Learning With Errors

Learning with Errors is a computational hardness assumption in lattice-based cryptography that belongs to the class of cryptographic primitives based on structured noise problems, distinguished by its reliance on the difficulty of solving linear equations perturbed by small, intentionally introduced errors over finite fields.

81

Publications

11.1K

Citations

227

Authors

140

Institutions

Publications per year

2017–2026

42

Authors

227

  1. CP
    Chris Peikert

    Georgia Institute of Technology, United States

    8 publications · 2.6K citations

    8

    h-index

  2. OR
    Oded Regev

    Tel Aviv University, Israel

    8 publications · 6.2K citations

    8

    h-index

  3. VV
    Vinod Vaikuntanathan

    Massachusetts Institute of Technology, United States

    6 publications · 559 citations

    6

    h-index

  4. ZB
    Zvika Brakerski

    Weizmann Institute of Science, Israel

    4 publications · 1K citations

    4

    h-index

  5. FG
    Florian Göpfert

    Technische Universität Darmstadt, Germany

    3 publications · 40 citations

    3

    h-index

1–5 of 227

Venues

  1. IACR Cryptology ePrint Archive

    Repository · 12 publications · 969 citations

    11

    h-index

  2. Journal of the ACM

    JACM · 3 publications · 3K citations

    3

    h-index

  3. Journal of Cryptology

    2 publications · 45 citations

    2

    h-index

  4. 2

    h-index

  5. arXiv (Cornell University)

    Repository · 2 publications · 62 citations

    2

    h-index

1–5