Learning with errors

Known as: LWE, Learning with error, R-LWE 
Learning with errors (LWE) is a problem in machine learning that is conjectured to be hard to solve. Introduced by Oded Regev in 2005, it is a… (More)
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Papers overview

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2017
2017
We introduce a new variant MP-LWE of the Learning With Errors problem (LWE) making use of the Middle Product between polynomials… (More)
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Highly Cited
2014
Highly Cited
2014
Bootstrapping is a technique, originally due to Gentry (STOC 2009), for “refreshing” ciphertexts of a somewhat homomorphic… (More)
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Highly Cited
2013
Highly Cited
2013
We show that the Learning with Errors (LWE) problem is classically at least as hard as standard worst-case lattice problems… (More)
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Highly Cited
2013
Highly Cited
2013
We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem. In… (More)
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Highly Cited
2012
Highly Cited
2012
The “learning with errors” (LWE) problem is to distinguish random linear equations, which have been perturbed by a small amount… (More)
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Highly Cited
2011
Highly Cited
2011
We propose a lattice-based functional encryption scheme for inner product predicates whose security follows from the difficulty… (More)
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Highly Cited
2011
Highly Cited
2011
We give new algorithms for a variety of randomly-generated instances of computational problems using a linearization technique… (More)
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Highly Cited
2010
Highly Cited
2010
Starting with the work of Ishai-Sahai-Wagner and Micali-Reyzin, a new goal has been set within the theory of cryptography… (More)
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Review
2010
Review
2010
  • Oded Regev
  • IEEE 25th Annual Conference on Computational…
  • 2010
In this survey we describe the Learning with Errors (LWE) problem, discuss its properties, its hardness, and its cryptographic… (More)
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Highly Cited
2005