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Additionally, it conducts an in-depth analysis of a public medical dataset concerning privacy protection, assessing the effectiveness of <jats:italic>k<\/jats:italic>-anonymity and <jats:italic>l<\/jats:italic>-diversity privacy criteria and examining the influence of quasi-identifier (QID) attributes on privacy preservation. The study showcases techniques to achieve privacy standards, including generalization and suppression. Furthermore, it introduces a novel approach that utilizes the genetic algorithm (GA) and a non-dominated sorting technique to maximize both privacy and utility in health data through multi-objective optimization. After examining the results, this paper offers a guide for data owners on selecting attributes for medical data publication and choosing suitable privacy preservation strategies. Through the exploration of the GA and the non-dominated sorting approach, this paper suggests that the proposed GA can offer promising non-dominated solutions to the issue of health data privacy in the era of data-driven healthcare. A combination of these algorithms can enhance privacy protection and provide healthcare professionals and researchers with essential knowledge, ultimately benefiting patient care and ensuring a more secure database system.<\/jats:p>","DOI":"10.1007\/s41019-025-00283-0","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T16:24:26Z","timestamp":1743697466000},"page":"362-375","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Analysis and Multi-objective Protection of Public Medical Datasets from Privacy and Utility Perspectives"],"prefix":"10.1007","volume":"10","author":[{"given":"Samsad","family":"Jahan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5955-6295","authenticated-orcid":false,"given":"Yong-Feng","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Enamul","family":"Kabir","sequence":"additional","affiliation":[]},{"given":"Kate","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"issue":"1","key":"283_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/computers6010001","volume":"6","author":"A Anjum","year":"2017","unstructured":"Anjum A, Raschia G (2017) Banga: an efficient and flexible generalization-based algorithm for privacy preserving data publication. 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