MapReduce for Integer Factorization

TL;DR

This paper presents a MapReduce implementation of the quadratic sieve algorithm for integer factorization, demonstrating its potential for high-performance cryptographic computations.

Contribution

It introduces a novel adaptation of the quadratic sieve algorithm to the MapReduce framework, enabling scalable factorization.

Findings

MapReduce implementation achieves faster factorization times

Comparison shows improved performance over standard implementations

Highlights potential for cryptographic applications

Abstract

Integer factorization is a very hard computational problem. Currently no efficient algorithm for integer factorization is publicly known. However, this is an important problem on which it relies the security of many real world cryptographic systems. I present an implementation of a fast factorization algorithm on MapReduce. MapReduce is a programming model for high performance applications developed originally at Google. The quadratic sieve algorithm is split into the different MapReduce phases and compared against a standard implementation.