Randomness extractors and data storage

Ariel Gabizon, Ronen Shaltiel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deterministic randomness extractors are functions E: {0, 1}4 → {0, 1}m which refine imperfect sources of randomness in the following sense: For every probability distribution X in some "interesting family" of distributions over {0, 1}n, applying E on a sample from X yields a distribution that is (close to) the uniform distribution. Randomness extractors have many applications in various areas of computer science. Recently, Shpilka [Shp13] showed how to apply randomness extractors to solve problems in the area of data storage. Following work by Shpilka [Shp14] and Gabizon and Shaltiel [GS12b] build on this connection and extend Shpilka's original paper. In this article, we give some relevant background on randomness extractors and explain how extractors (and closely related dispersers) can be applied to solve problems in data storage.

Original languageEnglish
Title of host publication2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479959877
DOIs
StatePublished - 2014
Event2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014 - Eilat, Israel
Duration: 3 Dec 20145 Dec 2014

Publication series

Name2014 IEEE 28th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014

Conference

Conference2014 28th IEEE Convention of Electrical and Electronics Engineers in Israel, IEEEI 2014
Country/TerritoryIsrael
CityEilat
Period3/12/145/12/14

Bibliographical note

Publisher Copyright:
© Copyright 2015 IEEE All rights reserved.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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