A Scoping Review of the Ethical, Legal, and Technical Dimensions of Privacy in Big Data Health Research

Abstract

Background: The proliferation of big data in health research—encompassing genomic datasets, electronic health records (EHRs), wearables, and multi-omics—offers unprecedented potential for scientific discovery and personalized medicine. However, this data-driven paradigm poses profound and novel challenges to the privacy of individuals, demanding an integrated analysis of ethical, legal, and technical safeguards. Aim: This scoping review synthesizes contemporary literature (2015-2024) to map the ethical dilemmas, legal frameworks, and technical solutions concerning privacy in big data health research. Methods: A systematic search was conducted across PubMed, IEEE Xplore, Scopus, and Google Scholar. Literature was thematically analyzed to identify key themes, tensions, and emergent strategies across the three dimensions. Results: The review identifies a core tension between data utility for the public good and individual privacy rights. Ethically, key issues include re-identification risk, informed consent for future unspecified research, and algorithmic bias. Legally, a fragmented global landscape exists, with regulations like the GDPR providing strong protections but creating compliance complexity. Technically, privacy-enhancing technologies (PETs) such as federated learning, differential privacy, and homomorphic encryption offer promising, yet imperfect, solutions. Conclusion: Effective privacy preservation in big data health research requires a harmonized, interdisciplinary approach. A robust governance framework must interweave ethical principles, adaptable legal compliance, and state-of-the-art technical controls, foster public trust while enabling responsible innovation.

Ähnliche Arbeiten