Learning With Errors Problem

The Learning with Errors Problem is a computational hardness assumption in lattice-based cryptography that belongs to the class of cryptographic problems based on structured noise, distinguished by its formulation of recovering a secret vector from a series of noisy linear equations over a finite field.

94

Publications

11.7K

Citations

234

Authors

149

Institutions

Publications per year

2017–2026

47

Authors

234

  1. OR
    Oded Regev

    Tel Aviv University, Israel

    8 publications · 6.2K citations

    8

    h-index

  2. CP
    Chris Peikert

    Georgia Institute of Technology, United States

    8 publications · 2.6K citations

    8

    h-index

  3. VV
    Vinod Vaikuntanathan

    Massachusetts Institute of Technology, United States

    5 publications · 222 citations

    5

    h-index

  4. TG
    Tim Güneysu

    Ruhr University Bochum, Germany

    4 publications · 146 citations

    4

    h-index

  5. BW
    Brent Waters

    The University of Texas at Austin, United States

    3 publications · 142 citations

    3

    h-index

1–5 of 234

Venues

  1. IACR Cryptology ePrint Archive

    Repository · 11 publications · 631 citations

    11

    h-index

  2. IEEE Access

    4 publications · 69 citations

    4

    h-index

  3. SIAM Journal on Computing

    4 publications · 204 citations

    4

    h-index

  4. Journal of the ACM

    JACM · 3 publications · 3K citations

    3

    h-index

  5. IEEE Systems Journal

    ISJ · 3 publications · 153 citations

    3

    h-index

1–5