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Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations
Dissertation (MSc (Computer Science))--University of Pretoria, 2025.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2026
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| _version_ | 1869483937555808256 |
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| access_status_str | Open Access |
| author2 | Venter, Hein S. |
| author_browse | Venter, Hein S. |
| author_facet | Venter, Hein S. |
| collection | Thesis |
| dc_rights_str_mv | © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc (Computer Science))--University of Pretoria, 2025. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/108657 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-07-01T04:06:56.181Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/108657 Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations Venter, Hein S. augustinmautjane@gmail.com Sibiya, George Mautjane, Augustin Thabang UCTD Sustainable Development Goals (SDGs) Data mining Privacy preserving Anonymisation Electronic healthcare Dissertation (MSc (Computer Science))--University of Pretoria, 2025. The growing dependence on data-driven methods in digital forensic investigations, particularly in sensitive sectors like healthcare, underscores the critical need to balance individual privacy with data utility. Traditional anonymisation techniques often degrade data quality, hampering effective forensic analysis. This research introduces the Extended Anonymisation Privacy Model (e-ANOP), a novel hybrid framework integrating generalization and suppression techniques to optimize the privacy-utility trade-off. Unlike conventional k-anonymity approaches, e-ANOP prioritizes the protection of sensitive attributes while preserving essential data patterns. Evaluated through a healthcare-based forensic investigation case study, e-ANOP demonstrated superior analytical integrity, maintaining high data utility without compromising privacy. These results highlight e-ANOP’s potential as a scalable, practical solution for privacy-preserving data analysis in digital forensics, offering significant advancements in safeguarding sensitive information while supporting robust investigative outcomes. The e-ANOP model addresses the limitations of existing anonymisation methods by introducing a dynamic, context-aware approach tailored to the complexities of digital forensic investigations. By leveraging adaptive generalization hierarchies and selective suppression, e-ANOP ensures that sensitive attributes—such as patient identifiers in healthcare datasets—are effectively anonymized while retaining critical patterns necessary for forensic analysis, such as temporal or behavioral trends. The model’s flexibility allows it to adapt to varying data structures and privacy requirements, making it applicable across diverse forensic scenarios. Furthermore, e-ANOP incorporates metrics to quantify both privacy preservation and data utility, enabling investigators to fine-tune the model based on specific case needs. Through rigorous testing on real-world healthcare datasets, e-ANOP achieved a significant reduction in re-identification risk while maintaining over 90% of the original data’s analytical value, positioning it as a robust tool for privacy-conscious digital forensics in high-stakes environments. Council of scientific and industrial research (CSIR) Computer Science MSc (Computer Science) Unrestricted Faculty of Engineering, Built Environment and Information Technology SDG-09: Industry, innovation and infrastructure 2026-02-26T08:13:01Z 2026-02-26T08:13:01Z 2026-05-04 2025-10-02 Dissertation * A2026 http://hdl.handle.net/2263/108657 en © 2024 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Sustainable Development Goals (SDGs) Data mining Privacy preserving Anonymisation Electronic healthcare Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| title | Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| title_full | Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| title_fullStr | Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| title_full_unstemmed | Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| title_short | Anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| title_sort | anonymisation algorithm for balancing data utility and privacy in electronic health digital forensic investigations |
| topic | UCTD Sustainable Development Goals (SDGs) Data mining Privacy preserving Anonymisation Electronic healthcare |
| url | http://hdl.handle.net/2263/108657 |