Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

Scalable Big Data Privacy with MapReduce

  • Sibghat Ullah Bazai
  • Julian Jang-Jaccard
  • Xuyun Zhang
Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_243-1

Overview

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

Introduction

Big data analytics is an emerging technology for finding new insights from large amounts of data. Processing and analyzing these large amounts of data require an extra set of tools and services....

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Sibghat Ullah Bazai
    • 1
  • Julian Jang-Jaccard
    • 1
  • Xuyun Zhang
    • 2
  1. 1.Institute of Natural and Mathematical SciencesMassey UniversityAucklandNew Zealand
  2. 2.Department of Electrical and Computer EngineeringUniversity of AucklandAucklandNew Zealand

Section editors and affiliations

  • Junjun Chen
    • 1
  • Deepak Puthal
    • 2
  1. 1.Swinburne University of TechnologyMelbourneAustralia
  2. 2.Faculty of Engineering and Information TechnologiesUniversity of Technology Sydney