Randomness extractors and data storage

Deterministic randomness extractors are functions E : {0, 1}ⁿ → {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.