Classifying and evaluating privacy-preserving techniques based on protection methods: a comprehensive study

SHERMINA JEBA; MOHAMMED BINJUBIER; MOHD ARFIAN ISMAIL; RESHMY KRISHNAN; SARACHANDRAN NAIR; GIRIJA NARASIMHAN

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Classifying and evaluating privacy-preserving techniques based on protection methods: a comprehensive study

Author(s)

SHERMINA JEBA Department Of Computing, Muscat College,Muscat,Oman

MOHAMMED BINJUBIER Faculty Of Computing,Universiti Malaysia Pahang, Kuantan, Pahang,Malaysia

MOHD ARFIAN ISMAIL Faculty Of Computing,Universiti Malaysia Pahang, Kuantan, Pahang,Malaysia

RESHMY KRISHNAN Department Of Computing, Muscat College,Muscat,Oman

SARACHANDRAN NAIR Department Of Computing, Muscat College,Muscat,Oman

GIRIJA NARASIMHAN Information Technology Department, University Of Technology And Applied Sciences,Muscat,Oman

Abstract

Many data analysis applications encounter the challenge of preserving the privacy of information. Over the past few years, many partially published data have become subjects of various concerns, ranging from unlawful access to private data to privacy breaches and unintended use of personal information. This problem has limited progress in advancing published data, prompting the need for robust privacy-protection techniques, which can minimize the chances of identifying sensitive individual information by unauthorized persons. The simplest solution to preserving sensitive information is to avoid public disclosure of such information. However, this might constitute a problem for data analysis, as there may not be available datasets to analyze and discover interesting patterns. Sometimes, the dataset must be disclosed under government regulations to enable access and subsequent analysis. Sometimes, the data owner may modify the data to ensure privacy and retain sufficient information for a safe release to the public. This process is usually referred to as privacy-preserving data publishing (PPDP). The review in this paper has rigorously evaluated some existing preserving privacy techniques and classified them based on their methods to reduce the risk of disclosing information. Moreover, the review focused on the methods of the current preserving privacy techniques to protect data and preserve the privacy of sensitive information, which is considered a key contribution of this study as it is expected to guide scholars to gain a deeper knowledge of the existing privacy preservation methods. This study also compared and analyzed various privacy-preserving techniques in terms of their advantages and drawbacks.

Publication Details

Page(s) 6510-6526

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 21, Year: 2022

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Keywords

Anonymization, Big Data Privacy Preservation, Data Publishing

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