Privacy preserving data access mechanism for health data

Sammanfattning: Due to the rise of digitalization and the growing amount of data, ensuring the integrity and security of patient data has become increasingly vital within the healthcare industry, which has traditionally managed substantial quantities of sensitive patient and personal information. This bachelor's thesis focused on designing and implementing a secure data sharing infrastructure to protect the integrity and confidentiality of patient data. Synthetic data was used to enable access for researchers and students in regulated environments without compromising patient privacy. The project successfully achieved its goals by evaluating different privacy-preserving mechanisms and developing a machine learning-based application to demonstrate the functionality of the secure data sharing infrastructure. Despite some challenges, the chosen algorithms showed promising results in terms of privacy preservation and statistical similarity. Ultimately, the use of synthetic data can promote fair decision-making processes and contribute to secure data sharing practices in the healthcare industry.