Scalable big data privacy with MapReduce

Sibghat Ullah Bazai; Julian Jang-Jaccard; Xuyun Zhang

doi:10.1007/978-3-319-77525-8_243

Scalable big data privacy with MapReduce

Sibghat Ullah Bazai, Julian Jang-Jaccard, Xuyun Zhang

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary/reference book › peer-review

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 language	English
Title of host publication	Encyclopedia of big data technologies
Editors	Sherif Sakr, Albert Y. Zomaya
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	1454-1462
Number of pages	9
ISBN (Electronic)	9783319775258
ISBN (Print)	9783319775241
DOIs	https://doi.org/10.1007/978-3-319-77525-8_243
Publication status	Published - 2019
Externally published	Yes

Access to Document

10.1007/978-3-319-77525-8_243

Cite this

@inbook{7f822dc85a504742b17e6371a9c3dbcc,

title = "Scalable big data privacy with MapReduce",

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 tomitigate these issues. We also provide future guidelines for anonymized data processing to ensure individual privacy in MapReduce.",

author = "Bazai, {Sibghat Ullah} and Julian Jang-Jaccard and Xuyun Zhang",

year = "2019",

doi = "10.1007/978-3-319-77525-8_243",

language = "English",

isbn = "9783319775241",

pages = "1454--1462",

editor = "Sherif Sakr and Zomaya, {Albert Y.}",

booktitle = "Encyclopedia of big data technologies",

publisher = "Springer, Springer Nature",

address = "United States",

}

Scalable big data privacy with MapReduce. / Bazai, Sibghat Ullah; Jang-Jaccard, Julian; Zhang, Xuyun.
Encyclopedia of big data technologies. ed. / Sherif Sakr; Albert Y. Zomaya. Cham, Switzerland: Springer, Springer Nature, 2019. p. 1454-1462.

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary/reference book › peer-review

TY - CHAP

T1 - Scalable big data privacy with MapReduce

AU - Bazai, Sibghat Ullah

AU - Jang-Jaccard, Julian

AU - Zhang, Xuyun

PY - 2019

Y1 - 2019

N2 - 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 tomitigate these issues. We also provide future guidelines for anonymized data processing to ensure individual privacy in MapReduce.

AB - 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 tomitigate these issues. We also provide future guidelines for anonymized data processing to ensure individual privacy in MapReduce.

U2 - 10.1007/978-3-319-77525-8_243

DO - 10.1007/978-3-319-77525-8_243

M3 - Entry for encyclopedia/dictionary/reference book

SN - 9783319775241

SP - 1454

EP - 1462

BT - Encyclopedia of big data technologies

A2 - Sakr, Sherif

A2 - Zomaya, Albert Y.

PB - Springer, Springer Nature

CY - Cham, Switzerland

ER -

Scalable big data privacy with MapReduce

Abstract

Access to Document

Fingerprint

Cite this