Recent Advances and Trends in Privacy-Preserving Data Publishing for Healthcare with Enhanced Data Utility

Abstract

The rapid digitization of healthcare infrastructures has resulted in an unprecedented increase in the volume of personal health information being generated and exchanged for various purposes, including clinical assessments, research activities, and administrative operations. While this volume of data offers significant potential to advance medical research and improve public health initiatives, it also raises substantial concerns about patient confidentiality and privacy. To address these critical issues, Privacy-Preserving Data Publishing (PPDP) has emerged as an essential framework that enables secure data sharing and analysis while protecting individual privacy. This survey provides a comprehensive examination of current research directions in PPDP within the healthcare sector, emphasizing techniques that balance privacy assurances with high data utility. We classify and evaluate recent advancements in methods such as differential privacy, federated learning, homomorphic encryption, secure multi-party computation, synthetic data generation, and data anonymization. For each approach, we analyze the foundational principles, recent innovations, strengths, limitations, and overall impact on data utility. Moreover, we identify prevailing challenges encountered by researchers and practitioners in this domain and propose potential avenues for future research poised to influence the development of privacy-preservation methodologies in healthcare data-sharing contexts. Future studies should aim to enhance the interpretability and transparency of privacy guarantees, not only for healthcare providers but also for legislative authorities. To enable objective comparisons among the discussed methods, establishing standardized evaluation criteria that encompass both utility and privacy considerations is imperative.