A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy

Akinwunmi Oluwafemi; Onashoga S.A,; Folorunso O.

A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy

Akinwunmi Oluwafemi *

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Onashoga S.A,

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Folorunso O.

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A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy

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Abstract

Big data frequently contains huge amounts of personal identifiable information and therefore the protection of user’s privacy becomes a challenge. Lots of researches had been administered on securing big data, but still limited in efficient privacy management and data sensitivity. This study designed a big data framework named Big Data-ARpM that is secured and enforces privacy and access restriction level. The internal components of Big Data-ARpM consists of six modules. Data Pre-processor which contains a data cleaning component that checks each entity of the data for conformity. Data Classifier deals with the classification of data due to the sensitivity of such data. Data Preservation consists of two sub modules with the goal of preserving data before release to any user or any third party application to prevent privacy violation of the data owner. Access Restriction module coordinates the user or third party application registration, access to data and information in the entire system.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Akinwunmi Oluwafemi, Onashoga S.A,, Folorunso O.. 2020. "A Secure Big Data Framework Based on Access Restriction And Preserved Level of Privacy". Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 20 (GJCST Volume 20 Issue E3).

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GJCST Volume 20 Issue E3

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Keywords

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Classification

GJCST-E Classification D.4.6

Submission ReceivedDecember 9, 2019
Peer Review Double Blind
Handling Editor
Accepted January 5, 2020
Published January 15, 2020

Version of record

v1.2

Issue date

December 5, 2020

Language

English

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