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IACR Communications in Cryptology
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Article . 2025
Data sources: DBLP
DBLP
Article . 2025
Data sources: DBLP
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Learning with Errors from Nonassociative Algebras

Authors: Andrew Mendelsohn; Cong Ling 0001;

Learning with Errors from Nonassociative Algebras

Abstract

We construct a provably-secure structured variant of Learning with Errors (LWE) using nonassociative cyclic division algebras, assuming the hardness of worst-case structured lattice problems, for which we are able to give a full search-to-decision reduction, improving upon the construction of Grover et al. named `Cyclic Learning with Errors' (CLWE). We are thus able to create structured LWE over cyclic algebras without any restriction on the size of secret spaces, which was required for CLWE as a result of its restricted security proof. We reduce the shortest independent vectors problem in ideal lattices, obtained from ideals in orders of such algebras, to the decision variant of LWE defined for nonassociative CDAs. We believe this variant has greater security and greater freedom with parameter choices than CLWE, and greater asymptotic efficiency of multiplication than module LWE. Our reduction requires new results in the ideal theory of such nonassociative algebras, which may be of independent interest. We then adapt an LPR-like PKE scheme to hold for nonassociative spaces, and discuss the efficiency and security of our construction, showing that it is immune to certain subfield attacks. Finally, we give example parameters to construct algebras for cryptographic use.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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