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
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11.7K
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234
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149
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2017–2026
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94
On lattices, learning with errors, random linear codes, and cryptography
Oded Regev · 2005 · 2.2K citations
On lattices, learning with errors, random linear codes, and cryptography
Oded Regev · Journal of the ACM · 2009 · 2.2K citations
On Ideal Lattices and Learning with Errors over Rings
Vadim Lyubashevsky, Chris Peikert, Oded Regev · Journal of the ACM · 2013 · 823 citations
Public-key cryptosystems from the worst-case shortest vector problem
Chris Peikert · 2009 · 804 citations
On the concrete hardness of Learning with Errors
M. Albrecht, Rachel Player, Sam Scott · Journal of Mathematical Cryptology · 2015 · 789 citations · Full text
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Massachusetts Institute of Technology, United States
5 publications · 222 citations
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Massachusetts Institute of Technology
Cambridge, United States
6 h-index
9 publications
602 citations
Georgia Institute of Technology
Atlanta, United States
5 h-index
5 publications
1.6K citations
New York University
New York, United States
5 h-index
7 publications
1.5K citations
Stanford University
Stanford, United States
5 h-index
10 publications
1.1K citations
University of California, Los Angeles
Los Angeles, United States
4 h-index
6 publications
573 citations
The University of Texas at Austin
Austin, United States
4 h-index
11 publications
440 citations
Courant Institute of Mathematical Sciences
New York, United States
4 h-index
4 publications
1.5K citations
University of California, Berkeley
Berkeley, United States
4 h-index
5 publications
799 citations
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Repository · 11 publications · 631 citations
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4 publications · 69 citations
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4 publications · 204 citations
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JACM · 3 publications · 3K citations
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ISJ · 3 publications · 153 citations
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