NDLI: Privacy preserving big data publishing: a scalable k-anonymization approach using MapReduce

Content Provider	IET Digital Library
Author	Mehta, Brijesh B. Rao, Udai Pratap
Abstract	Big data is collected and processed using different sources and tools that lead to privacy issues. Privacy preserving data publishing techniques such as k-anonymity, l-diversity, and t-closeness are used to de-identify the data; however, the chances of re-identification are always remain present since data is collected from multiple sources. Owing to the large volume of data, less generalisation or suppression is required to achieve the same level of privacy, which is also known as ‘large crowd effect’, although it is always challenging to handle such a large data for anonymization. MapReduce handles large volume of data and distributes the data into the smaller chunks across the multiple nodes; consequently, the full advantage of large volume of data is underachieved. Therefore, scalability of privacy preserving techniques becomes a challenging area of research. The authors explore this area and propose an algorithm named scalable k-anonymization (SKA) using MapReduce for privacy preserving big data publishing. The authors also compare the approach with existing approaches that results into a remarkable improvement of the data utility and significantly enhances the performance in terms of running time.
Starting Page	271
Ending Page	276
Page Count	6
ISSN	17518806
Volume Number	11
e-ISSN	17518814
Issue Number	Issue 5, Oct (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-sen/11/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2016.0264
Journal	IET Software
Publisher Date	2017-07-31
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Big Data Data Collection Data Privacy Data Security I-diversity Large Crowd Effect MapReduce Parallel Processing Parallel Software Privacy Preserving Data Publishing Technique Publishing Scalable K-anonymization Approach SKA T-closeness
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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