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Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
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A Survey on Two-Phase Top-Down Specialization For Data Anonymization Using Map Reduce On Cloud

Published on December 2014 by Monali S.bachhav, Amitkumar Manekar
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Innovations and Trends in Computer and Communication Engineering
Foundation of Computer Science USA
ITCCE - Number 4
December 2014
Authors: Monali S.bachhav, Amitkumar Manekar
ecf4ec63-f4c8-4a8b-ab5b-2f78ae4bf064

Monali S.bachhav, Amitkumar Manekar . A Survey on Two-Phase Top-Down Specialization For Data Anonymization Using Map Reduce On Cloud. Innovations and Trends in Computer and Communication Engineering. ITCCE, 4 (December 2014), 8-11.

@article{
author = { Monali S.bachhav, Amitkumar Manekar },
title = { A Survey on Two-Phase Top-Down Specialization For Data Anonymization Using Map Reduce On Cloud },
journal = { Innovations and Trends in Computer and Communication Engineering },
issue_date = { December 2014 },
volume = { ITCCE },
number = { 4 },
month = { December },
year = { 2014 },
issn = 0975-8887,
pages = { 8-11 },
numpages = 4,
url = { /proceedings/itcce/number4/19060-2026/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Innovations and Trends in Computer and Communication Engineering
%A Monali S.bachhav
%A Amitkumar Manekar
%T A Survey on Two-Phase Top-Down Specialization For Data Anonymization Using Map Reduce On Cloud
%J Innovations and Trends in Computer and Communication Engineering
%@ 0975-8887
%V ITCCE
%N 4
%P 8-11
%D 2014
%I International Journal of Computer Applications
Abstract

Most cloud services require users to share personal data like electronic health records for analysis of data or mining, bringing privacy concerns. In many cloud applications at present the scale of data increases in accordance with Big Data, thereby making it a complicated to commonly used software tools to handle and process a large-scale data within a tolerable elapsed time. It is challenging for previous annonymization approaches to achieve privacy preservation on large scale data sets due to insufficiency. The proposed a scalable two-phase top-down specialization (TDS) approach uses MapReduce architecture on cloud to annonymized large scale datasets finally deliberately design a group of innovative MapReduce jobs to particularly accomplish specialization computation in a highly scalable way. So the ability of TDS and efficiency of TDS can be significantly improved over existing approaches.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Anonymization Top-down Specialization Mapreduce Cloud Privacy Preservation