Work

Systematization of Pseudonymization Algorithms for Health Data

Year: 2025

Type: book-chapter

Abstract: Biomedical research depends on collecting, analyzing, and sharing of data while protecting patient or study participant privacy. Laws such as the European Union's General Data Protection Regulation recommend pseudonymization, which provides a mechanism to protect privacy by replacing personally identifiable information with pseudonyms. We present a comparative analysis of pseudonymization algorithms categorized into encryption-, hash-, counter-, and randomness-based methods, structured along eight key axes such as pseudonym length, complexity, and suitability for automation or manual transmission. This analysis highlights the strengths and limitations of each method, assisting researchers in making informed decisions about pseudonymization for health data.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250458

Authors: Armin Müller, Fabian Prasser

Institutions: Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charité - Universitätsmedizin Berlin

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Bundesministerium für Bildung und Forschung