Scalable big data privacy with MapReduce

Sibghat Ullah Bazai, Julian Jang-Jaccard, Xuyun Zhang

Research output: Chapter in Book/Report/Conference proceedingEntry for encyclopedia/dictionary/reference book

Abstract

Processing big data to drive useful information has been in spotlight in recent years. Numerous approaches have been proposed to explore different ways to analyse the big data. However, data privacy has been an issue during the process because data could have been from various sources and they may contain sensitive personal information of individual. Hadoop MapReduce has been considered as one of the most promising approaches for big data processing. This chapter provides an overview of MapReduce environment, privacy challenges faced during the processing of data in MapReduce cluster, existing approaches adopted by various researchers to
mitigate these issues. We also provide future guidelines for anonymized data processing to ensure individual privacy in MapReduce.
Original languageEnglish
Title of host publicationEncyclopedia of big data technologies
EditorsSherif Sakr, Albert Y. Zomaya
Place of PublicationCham, Switzerland
PublisherSpringer, Springer Nature
Pages1454-1462
Number of pages9
ISBN (Electronic)9783319775258
ISBN (Print)9783319775241
DOIs
Publication statusPublished - 2019
Externally publishedYes

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    Bazai, S. U., Jang-Jaccard, J., & Zhang, X. (2019). Scalable big data privacy with MapReduce. In S. Sakr, & A. Y. Zomaya (Eds.), Encyclopedia of big data technologies (pp. 1454-1462). Cham, Switzerland: Springer, Springer Nature. https://doi.org/10.1007/978-3-319-77525-8_243