On lattices, learning with errors, random linear codes, and cryptography

Abstract

Our main result is a reduction from worst-case lattice problems such as GapSVP and SIVP to a certain learning problem. This learning problem is a natural extension of the “learning from parity with error” problem to higher moduli. It can also be viewed as the problem of decoding from a random linear code. This, we believe, gives a strong indication that these problems are hard. Our reduction, however, is quantum. Hence, an efficient solution to the learning problem implies a quantum algorithm for GapSVP and SIVP. A main open question is whether this reduction can be made classical (i.e., nonquantum).

Document Details

Document Type
Pub Defense Publication
Publication Date
Sep 01, 2009
Source ID
10.1145/1568318.1568324

Entities

People

  • Oded Regev

Organizations

  • Army Research Office
  • Tel Aviv University

Tags

Fields of Study

  • Computer science

Readers

  • Distributed Systems and Data Platform Development
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • Cyber
  • Cyber - Cryptography
  • Quantum Computing