HEALTH-DP: a framework for health data de-anonymization risk assessment and mitigation with differential privacy

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dc.contributor.authorAguelal, Hamzaen
dc.contributor.authorShafiq, Akashaen
dc.contributor.authorPalmieri, Paoloen
dc.contributor.funderEuropean Commissionen
dc.contributor.funderResearch Irelanden
dc.date.accessioned2026-02-25T11:40:04Z
dc.date.available2026-02-25T11:40:04Z
dc.date.issued2026en
dc.description.abstractPrivacy protection is a significant challenge in the computation of personal data, especially when data (e.g. health-related) is considered sensitive under relevant regulations. Although anonymization is widely applied, adversaries can still de-anonymize data through sophisticated attacks. Risks are particularly severe for health datasets, such as genomics or physiological data, due to their inherent uniqueness. Differential privacy (DP) has emerged as a strong privacy-preservation technique. However, current approaches to its implementation remain theoretical (and thus not directly linked to actual risks) or specific to a single context, and lack inclusive pathways for different stakeholders in the medical environment. This paper presents a comprehensive framework to address these limitations, combining a systematic study of re-identification attacks and practical risk assessment with DP implementation. The framework incorporates the parties’ roles, threat pre-assessment, known attacks and DP integration. An adaptive mitigation strategy within a structured flow and logical process ensures wide coverage of different requirements. Furthermore, we validate the framework by applying central DP (CDP) to a heart-attack prediction dataset as an initial case study for a future broader end-to-end implementation. The framework provides a roadmap for implementing DP based on evaluating re-identification risks and data governance requirements, and gives stakeholders actionable guidance for safer data use.en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationAguelal, H., Shafiq, A. and Palmieri, P. (2026) 'HEALTH-DP: a framework for health data de-anonymization risk assessment and mitigation with differential privacy', Proceedings of the 12th International Conference on Information Systems Security and Privacy (ICISSP 2026), Marbella, Spain, 4-6 March, Volume 1, pp. 201-212.en
dc.identifier.endpage212en
dc.identifier.isbn978-989-758-800-6en
dc.identifier.issn2184-4356en
dc.identifier.startpage201en
dc.identifier.urihttps://hdl.handle.net/10468/18568
dc.identifier.volume1en
dc.language.isoenen
dc.publisherSCITEPRESSen
dc.relation.ispartof12th International Conference on Information Systems Security and Privacy (ICISSP 2026)en
dc.relation.projectinfo:eu-repo/grantAgreement/EC/HEen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6222/IE/SFI Centre for Research Training in Advanced Networks for Sustainable Societies/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Centres for Research Training (CRT) Programme/18/CRT/6223/IE/SFI Centre for Research Training in Artificial Intelligence/en
dc.rights© 2026, SCITEPRESS – Science and Technology Publications, Lda.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectDe-anonymizationen
dc.subjectDifferential privacyen
dc.subjectAnonymizationen
dc.subjectRisk assessmenten
dc.subjectHealth dataen
dc.subjectPrivacy enhancement technologiesen
dc.titleHEALTH-DP: a framework for health data de-anonymization risk assessment and mitigation with differential privacyen
dc.typeConference itemen
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