Privacy and Security Policies in Big Data

326 Pages·2017·16.14 MB·English

by  Sharvari Tamane

Privacy and Security
Policies in Big Data
Sharvari Tamane
MGM’s Jawaharlal Nehru Engineering College, India
Vijender Kumar Solanki
Institute of Technology and Science Ghaziabad, India
Nilanjan Dey
Techno India College of Technology, India
A volume in the Advances in Information Security,
Privacy, and Ethics (AISPE) Book Series
Published in the United States of America by
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Library of Congress Cataloging-in-Publication Data
Names: Tamane, Sharvari, 1973- editor. | Solanki, Vijender Kumar 1980- editor. |
Dey, Nilanjan, 1984- editor.
Title: Privacy and security policies in big data / Sharvari Tamane, Vijender Kumar Solanki, and Nilanjan
Dey, editors.
Description: Hershey, PA : Information Science Reference, [2017] | Includes
bibliographical references and index.
Identifiers: LCCN 2017003838| ISBN 9781522524861 (hardcover) | ISBN
9781522524878 (ebook)
Subjects: LCSH: Big data--Security measures. | Data protection. | Privacy,
Right of. | Telecommunication policy.
Classification: LCC QA76.9.B45 P75 2017 | DDC 005.8--dc23 LC record available at https://lccn.loc.gov/2017003838

This book is published in the IGI Global book series Advances in Information Security, Privacy, and Ethics (AISPE) (ISSN:
1948-9730; eISSN: 1948-9749)
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the
authors, but not necessarily of the publisher.
For electronic access to this publication, please contact: [email protected].
Advances in Information
Security, Privacy, and Ethics
(AISPE) Book Series
Manish Gupta
State University of New York, USA
ISSN:1948-9730
EISSN:1948-9749
Mission
As digital technologies become more pervasive in everyday life and the Internet is utilized in ever in-
creasing ways by both private and public entities, concern over digital threats becomes more prevalent.
The Advances in Information Security, Privacy, & Ethics (AISPE) Book Series provides cutting-
edge research on the protection and misuse of information and technology across various industries and
settings. Comprised of scholarly research on topics such as identity management, cryptography, system
security, authentication, and data protection, this book series is ideal for reference by IT professionals,
academicians, and upper-level students.
Coverage
• Privacy-Enhancing Technologies
IGI Global is currently accepting manuscripts
• Data Storage of Minors
for publication within this series. To submit a pro-
• IT Risk
posal for a volume in this series, please contact our
• Risk Management
Acquisition Editors at [email protected]
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or visit: http://www.igi-global.com/publish/.
• Global Privacy Concerns
• Computer ethics
• Device Fingerprinting
• CIA Triad of Information Security
• Privacy Issues of Social Networking
The Advances in Information Security, Privacy, and Ethics (AISPE) Book Series (ISSN 1948-9730) is published by IGI Global, 701 E.
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701 East Chocolate Avenue, Hershey, PA 17033, USA
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Table of Contents
Preface.................................................................................................................................................xiv
Acknowledgment................................................................................................................................xix
Section 1
Fundamentals of Big Data
Chapter 1
TheBasicsofBigDataandSecurityConcerns......................................................................................1
Sharvari C. Tamane, MGM’s Jawaharlal Nehru Engineering College, India
Vijender K. Solanki, Institute of Technology and Science Ghaziabad, India
Madhuri S. Joshi, MGM’s Jawaharlal Nehru Engineering College, India
Section 2
Big Data Tools and Technologies With Case Studies
Chapter 2
AffordancesofDataScienceinAgriculture,Manufacturing,andEducation......................................14
Krishnan Umachandran, Nelcast Ltd., India
Debra Sharon Ferdinand-James, The University of the West Indies, Trinidad and Tobago
Chapter 3
EvolutionofCloudinBigDataWithHadooponDockerPlatform.....................................................41
Meenu Gupta, Ansal University, India
Neha Singla, Punjabi University, India
Section 3
Non-Relational Databases
Chapter 4
MonogDB:DataManagementinNoSQL............................................................................................64
Sonali Tidke, SPPU, India


Chapter 5
ASecurity-By-DistributionApproachtoManageBigDatainaFederationofUntrustworthy
Clouds...................................................................................................................................................92
Jens Kohler, University of Applied Sciences Mannheim, Germany
Christian Richard Lorenz, University of Applied Sciences Mannheim, Germany
Markus Gumbel, University of Applied Sciences Mannheim, Germany
Thomas Specht, University of Applied Sciences Mannheim, Germany
Kiril Simov, Bulgarian Academy of Sciences, Bulgaria
Chapter 6
SelectiveDataConsistencyModelinNo-SQLDataStore.................................................................124
Shraddha Pankaj Phansalkar, Symbiosis International University, India
Ajay Dani, G. H. Raisoni Institute of Technology, India
Section 4
Data Mining
Chapter 7
EducationalDataMiningReview:TeachingEnhancement................................................................149
Rashmi Agrawal, Manav Rachna International University, India
Neha Gupta, Manav Rachna International University, India
Section 5
Big Data Privacy and Security
Chapter 8
PrivacyPreservingDataMiningonUnstructuredData.....................................................................167
Trupti Vishwambhar Kenekar, G. H. Raisoni Institute of Engineering and Technology, India
Ajay R. Dani, G. H. Raisoni Institute of Engineering and Technology, India
Chapter 9
DifferentialPrivacyApproachforBigDataPrivacyinHealthcare....................................................191
Marmar Moussa, University of Connecticut, USA
Steven A. Demurjian, University of Connecticut, USA
Section 6
Is IoT Driving a Big Data?
Chapter 10
InternetofThingsinRealLife:Applications.....................................................................................215
Abhijeet Chandrakant Dabre, Smt. Kashibai Navale College of Engineering, India
Sandesh Shivaji Mahamure, Smt. Kashibai Navale College of Engineering, India
Snehal Pandurang Wadibhasme, Smt. Kashibai Navale College of Engineering, India

Section 7
Big Data Analytics
Chapter 11
DevelopmentofDataAnalyticsinShipping......................................................................................239
Lokukaluge P. Perera, Norwegian Marine Technology Research Institute (MARINTEK),
Norway
Brage Mo, Norwegian Marine Technology Research Institute (MARINTEK), Norway
Chapter 12
BigDataPredictiveAnalysisforDetectionofProstateCanceronCloud-BasedPlatform:
MicrosoftAzure..................................................................................................................................259
Ritesh Anilkumar Gangwal, Dr. Babasaheb Ambedkar Marathwada University, India
Ratnadeep R. Deshmukh, Dr. Babasaheb Ambedkar Marathwada University, India
M. Emmanuel, Pune Institute of Computer Technology, India
Compilation of References...............................................................................................................279
About the Contributors....................................................................................................................299
Index...................................................................................................................................................304
Detailed Table of Contents
Preface.................................................................................................................................................xiv
Acknowledgment................................................................................................................................xix
Section 1
Fundamentals of Big Data
Chapter 1
TheBasicsofBigDataandSecurityConcerns......................................................................................1
Sharvari C. Tamane, MGM’s Jawaharlal Nehru Engineering College, India
Vijender K. Solanki, Institute of Technology and Science Ghaziabad, India
Madhuri S. Joshi, MGM’s Jawaharlal Nehru Engineering College, India
Thechapteriswrittenontwoimportantbuildings,thebasicsofBigdataandtheirsecurityconcern.The
chapterisclassifyingindifferentsections.Thechapterstartswiththebasicofbigdataandisconcluded
withsecurityconcern.Thechapterisenrichedwithdifferentcategoryexamplestomaketextseasyfor
authorunderstanding.Thechapterbeginswiththeintroductionofbigdata,theirmemorysizefollowed
bytheexamples.Thechapterexplainsthecategoryofbigdataintypeofstructured,semi-structuredand
unstructureddata.Thediscussiononoperationaldataserviceandbigdataapplicationisalsoincludedto
ensurethebasicunderstandingtoreaders.Thesecondportionofchapterwhichisbasedonsecurityinbig
data.It’sexplainingtheissuesandchallengesinbigdata.Thesectionalsofocusingontheshiftparadigm
fromcloudenvironmenttobigdataenvironmentchangesandtheproblemsencounterbyorganizations.
Thesectiondiscussestheframeworkissueandconcludedwiththenecessityofunderstandingsecurityin
thebigdata,keepinginviewofexpansionofinformationtechnologyinfrastructureinthe21stcentury.
Section 2
Big Data Tools and Technologies With Case Studies
Chapter 2
AffordancesofDataScienceinAgriculture,Manufacturing,andEducation......................................14
Krishnan Umachandran, Nelcast Ltd., India
Debra Sharon Ferdinand-James, The University of the West Indies, Trinidad and Tobago
Continuedtechnologicaladvancementsofthe21stCenturyaffordmassivedatagenerationinsectorsof
oureconomytoincludethedomainsofagriculture,manufacturing,andeducation.However,harnessing
suchlarge-scaledata,usingmoderntechnologiesforeffectivedecision-makingappearstobeanevolving



sciencethatrequiresknowledgeofBigDatamanagementandanalytics.Bigdatainagriculture,
manufacturing,andeducationarevariedsuchasvoluminoustext,images,andgraphs.ApplyingBig
datasciencetechniques(e.g.,functionalalgorithms)forextractingintelligencedataaffordsdecision
markersquickresponsetoproductivity,marketresilience,andstudentenrollmentchallengesintoday’s
unpredictablemarkets.ThischapterservestoemploydatascienceforpotentialsolutionstoBigData
applicationsinthesectorsofagriculture,manufacturingandeducationtoalesserextent,usingmodern
technologicaltoolssuchasHadoop,Hive,Sqoop,andMongoDB.
Chapter 3
EvolutionofCloudinBigDataWithHadooponDockerPlatform.....................................................41
Meenu Gupta, Ansal University, India
Neha Singla, Punjabi University, India
Datacanbeanythingbutfromalargedatabaseextractionofusefulinformationisknownasdatamining.
Cloudcomputingisatermwhichrepresentacollectionofhugeamountofdata.Cloudcomputingcan
becorrelatedwithdataminingandBigDataHadoop.Bigdataishighvolume,highvelocity,and/or
highvarietyinformationassetthatrequirenewformofprocessingtoenableenhanceddecisionmaking,
insightdiscoveryandprocessoptimization.Datagrowth,speedandcomplexityarebeingaccompanied
bydeploymentofsmartsensorsanddevicesthattransmitdatacommonlycalledtheInternetofThings,
multimediaandbyothersourcesofsemi-structuredandstructureddata.BigDataisdefinedasthecore
elementofnearlyeverydigitaltransformationtoday.
Section 3
Non-Relational Databases
Chapter 4
MonogDB:DataManagementinNoSQL............................................................................................64
Sonali Tidke, SPPU, India
MongoDBisaNoSQLtypeofdatabasemanagementsystemwhichdoesnotadheretothecommonly
usedrelationaldatabasemanagementmodel.MongoDBisusedforhorizontalscalingacrossalarge
numberofserverswhichmayhavetens,hundredsoreventhousandsofservers.Thishorizontalscalingis
performedusingsharding.Shardingisadatabasepartitioningtechniquewhichpartitionslargedatabase
intosmallerpartswhichareeasytomanageandfastertoaccess.TherearehundredsofNoSQLdatabases
availableinthemarket.ButeachNoSQLproductisdifferentintermsoffeatures,implementationsand
behavior.NoSQLandRDBMSsolvedifferentsetofproblemsandhavedifferentrequirements.MongoDB
hasapowerfulquerylanguagewhichextendsSQLtoJSONenablingdeveloperstotakebenefitofpower
ofSQLandflexibilityofJSON.Alongwithsupportforselect/from/wheretypeofqueries,MongoDB
supportsaggregation,sorting,joinsaswellasnestedarrayandcollections.Toimprovequeryperformance,
indexesandmanymorefeaturesarealsoavailable.