Journal of Engineering Science (Chişinău) (Sep 2022)
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.
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