Privacy 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.