De-Anonymization of Health Data: A Survey of Practical Attacks, Vulnerabilities and Challenges

Health data ranks among the most sensitive personal information disclosing serious details about individuals. Although anonymization is used, vulnerabilities persist, leading to de-anonymization and privacy risks highlighted by regulations like the General Data Protection Regulation (GDPR). This survey examines deanonymization attacks on health datasets, focusing on methodologies employed, data targeted, and the effectiveness of current anonymization practices. Unlike previous surveys that lack consensus on essential empirical questions, we provide a comprehensive summary of practical attacks, offering a more logical perspective on real-world risk. Our investigation systematically categorizes these practical attacks, revealing insights into success rates, generality and reproducibility, new analytics used, and the specific vulnerabilities they exploit. The study covers health-related datasets, including medical records, genomic data, electrocardiograms (ECGs), and neuroimaging, highlighting the need for more robust anonymization. Significant challenges remain in the literature despite existing reviews. We advocate for stronger data safeness by improving anonymization methods and advancing research on de-anonymization and assessment within healthcare.