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Journal of Engineering Science (Chişinău)
Article . 2022 . Peer-reviewed
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https://dx.doi.org/10.60692/x2...
Other literature type . 2022
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https://dx.doi.org/10.60692/s5...
Other literature type . 2022
Data sources: Datacite
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APPLICATION OF PRIVACY-PRESERVING DATA PUBLISHING IN TERTIARY INSTITUTIONS OF KEBBI STATE USING GENERALIZATION AND SUPPRESSION

تطبيق نشر البيانات المحفوظة للخصوصية في مؤسسات التعليم العالي في ولاية كيبي باستخدام التعميم والقمع
Authors: Anas Shehu; Alhassan Salihu; Abubakar Sani;

APPLICATION OF PRIVACY-PRESERVING DATA PUBLISHING IN TERTIARY INSTITUTIONS OF KEBBI STATE USING GENERALIZATION AND SUPPRESSION

Abstract

The research was conducted in the field of publishing data to preserve confidentiality. Several educational datasets have been used to address privacy and utility. The sample questionnaires served to investigate the level of privacy awareness and enforcement in the records of students in tertiary institutions in Kebbi State, Nigeria. The benchmark datasets were obtained from Kebbi State Polytechnic Dakin-gari. K-anonymity and l-diversity models were used with k configurations and suppression limits of 10 and 50% in the ARX 3.9.0 de-anonymization environment. The work evaluates data privacy, quality, and execution time for each k value and suppressions limit. Experimental results demonstrate that the higher the suppression the more balanced exists between privacy and utility. It was observed that suppression of 50% provides less anonymization time irrespective of k compared to k values in suppression = 10%. This was proved to be due to less time it takes anonymization to be completed Also, from respondents, 92% of students’ records were kept permanently in plain and, issued to third parties like that—with no privacy guarantee. This poses privacy threats to datasets.

Keywords

k-anonimitate, Data Quality Assessment and Improvement, Sociology and Political Science, Information privacy, FOS: Political science, Social Sciences, calitate, k-anonymity, Decision Sciences, Computer security, Differential Privacy, Law enforcement, Political science, k-Anonymity, utilitate, Privacy-Preserving Techniques for Data Analysis and Machine Learning, Engineering (General). Civil engineering (General), Quality, dakin-gari, Privacy, quality, Physical Sciences, Internet privacy, Arx, TA1-2040, Anonymity, Internet Users' Information Privacy Concerns, Confidentiality, Enforcement, Dakin-gari, FOS: Law, Management Science and Operations Research, privacy, Arx de-anonymization tool, Artificial Intelligence, Data publishing, Privacy Preserving, Publishing, instrument de dezanonimizare, arx de-anonymization tool, Data Privacy, Privacy Concerns, QA75.5-76.95, Computer science, confidenţialitate, utility, Electronic computers. Computer science, Computer Science, Law

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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gold