Open Access

ARTICLE

A Pursuit of Sustainable Privacy Protection in Big Data Environment by an Optimized Clustered-Purpose Based Algorithm

by Norjihan Binti Abdul Ghani¹, Muneer Ahmad¹, Zahra Mahmoud¹, Raja Majid Mehmood^2,*

1 Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, 50603, Malaysia
2 Information and Communication Technology Department, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, 43900, Malaysia

* Corresponding Authors: Raja Majid Mehmood. Email: ,

(This article belongs to the Special Issue: Soft Computing in Intrusion Detection)

Intelligent Automation & Soft Computing 2020, 26(6), 1217-1231. https://doi.org/10.32604/iasc.2020.011731

Received 26 May 2020; Accepted 07 July 2020; Issue published 24 December 2020

Abstract

Achievement of sustainable privacy preservation is mostly very challenging in a resource shared computer environment. This challenge demands a dedicated focus on the exponential growth of big data. Despite the existence of specific privacy preservation policies at the organizational level, still sustainable protection of a user’s data at various levels, i.e., data collection, utilization, reuse, and disclosure, etc. have not been implemented to its spirit. For every personal data being collected and used, organizations must ensure that they are complying with their defined obligations. We are proposing a new clustered-purpose based access control for users’ sustainable data privacy protection in a big data environment. The clustered-purpose based access control significantly contributes to handling the personal data for stated, unambiguous, and genuine purposes. The proposed algorithm picks specific records from the sample space. It ensures the sustainability and utilization of data for intended purposes by validating the existing privacy tags, assigning new privacy tags based on a clustered-purpose based approach. The proposed method equally ensures the security and sustainable privacy aspects of existing as well as new personal data managed inside large databases repositories. The comparative analysis of significant results presents the outperformance of the proposed algorithm as compared to existing non-purpose based conventional methods of sustainable privacy preservation. The proposed algorithm clusters the large datasets in a big data environment and allows only authorized access to users. The current study is limited to purpose-based access control based on privacy tags. However, future research can also consider other types of privacy protection scenarios in a shared environment.

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Citations