Application of Privacy-Preserving Data Publishing on Students’ Data in Tertiary Institutions of Kebbi State Using K-Anonymity

Authors : Anas Shehu; Alhassan Salihu; Abubakar Sani

Volume/Issue : Volume 7 - 2022, Issue 6 - June

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The research was conducted in privacypreserving data publishing, to our knowledge only a few used educational datasets to address privacy and utility. This research used sample questionnaires to investigate the awareness of privacy and its application to student records and also applying privacy to students’ datasets of all tertiary institutions of Kebbi State, Nigeria. Student datasets were obtained from Kebbi State Polytechnic Dakin-gari which we used as a benchmark. 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 two variants of 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 when suppression = 10%. This was proved to be due to less time it takes anonymization to be completed. Also, our work ranks six institutions from 1st through 6th based on some parameters obtained via questionnaire/responses on privacy threats. The work however established that all students’ records are faced with serious privacy threats as no institution employ any privacy-preserving techniques. Consequently, the research proposed a privacy framework for all six schools to deploy for better preservation

Keywords : Arx de-anonymization tool, Dakin-gari, kanonymity, privacy, quality, utility


Paper Submission Last Date
31 - March - 2024

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