Analysis and Protection of Public Medical Dataset: From Privacy Perspective
Paper
Jahan, Samsad, Ge, Yong-Feng, Kabir, Enamul and Wang, Hua. 2023. "Analysis and Protection of Public Medical Dataset: From Privacy Perspective." 12th International Conference on Health Information Science (HIS 2023). Melbourne, Australia 23 - 24 Oct 2023 Singapore. https://doi.org/10.1007/978-981-99-7108-4_7
Paper/Presentation Title | Analysis and Protection of Public Medical Dataset: From Privacy Perspective |
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Presentation Type | Paper |
Authors | Jahan, Samsad, Ge, Yong-Feng, Kabir, Enamul and Wang, Hua |
Journal or Proceedings Title | Proceedings of the 12th International Conference on Health Information Science (HIS 2023) |
Journal Citation | 14305, pp. 79-90 |
Number of Pages | 12 |
Year | 2023 |
Place of Publication | Singapore |
ISBN | 9789819971084 |
9789819971077 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-99-7108-4_7 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-981-99-7108-4_7 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-981-99-7108-4 |
Conference/Event | 12th International Conference on Health Information Science (HIS 2023) |
Event Details | 12th International Conference on Health Information Science (HIS 2023) Parent International Conference on Health Information Science (HIS) Delivery In person Event Date 23 to end of 24 Oct 2023 Event Location Melbourne, Australia |
Abstract | High-quality medical treatment is unattainable without protecting patients’ medical records and other sensitive information. One of the most critical challenges in the medical industry is patient privacy in light of medical systems’ widespread digitization and networking. What we call “health data” includes a plethora of information on individuals, including their medical records, treatment records, genetic data, and demographic information. In this paper, we review existing methods to keep patients’ health records private and compare their advantages and limitations. We then analyze the public medical dataset from the perspective of privacy protection, utilizing the k-anonymity and l-diversity models, and compare the impact of quasi-identifier attributes on privacy protection. Furthermore, we conduct experiments to investigate the trade-off between privacy and utility. Based on the analysis results, this paper provides data owners with a guide on how to choose attributes for medical data publication and how to select the appropriate techniques for preserving privacy in medical data publication. |
Keywords | Data privacy; Medical data; k-anonymity; l-diversity |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460599. Data management and data science not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Victoria University |
School of Mathematics, Physics and Computing |
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https://research.usq.edu.au/item/zq429/analysis-and-protection-of-public-medical-dataset-from-privacy-perspective
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