O
Oded Regev
Researcher at New York University
Publications - 218
Citations - 20795
Oded Regev is an academic researcher from New York University. The author has contributed to research in topics: Lattice problem & Quantum computer. The author has an hindex of 60, co-authored 211 publications receiving 18156 citations. Previous affiliations of Oded Regev include Courant Institute of Mathematical Sciences & Tel Aviv University.
Papers
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Journal ArticleDOI
Worst-Case to Average-Case Reductions Based on Gaussian Measures
Daniele Micciancio,Oded Regev +1 more
TL;DR: It is shown that finding small solutions to random modular linear equations is at least as hard as approximating several lattice problems in the worst case within a factor almost linear in the dimension of the lattice, and it is proved that the distribution that one obtains after adding Gaussian noise to a lattice has the following interesting property.
Book ChapterDOI
Lattice-based cryptography
TL;DR: Some of the recent progress on lattice-based cryptography is described, starting from the seminal work of Ajtai, and ending with some recent constructions of very efficient cryptographic schemes.
Journal ArticleDOI
The Complexity of the Local Hamiltonian Problem
TL;DR: The complexity of the 2-locHam problem has been shown to be Ω(n 2 )-complete for any n ≥ 3 in the complexity class QMA as discussed by the authors.
Proceedings ArticleDOI
Worst-case to average-case reductions based on Gaussian measures
Daniele Micciancio,Oded Regev +1 more
TL;DR: It is shown that solving modular linear equation on the average is at least as hard as approximating several lattice problems in the worst case within a factor almost linear in the rank of the lattice, and it is proved that the distribution that one obtains after adding Gaussian noise to the lattices has the following interesting property.
Posted Content
Classical Hardness of Learning with Errors
TL;DR: It is shown that the Learning with Errors (LWE) problem is classically at least as hard as standard worst-case lattice problems, and the techniques captured the tradeoff between the dimension and the modulus of LWE instances, leading to a much better understanding of the landscape of the problem.