Gerlach Anonymization and Identifiability

Enhancing Data Protection Through Differential Privacy and Artificial Intelligence
1. Auflage 2025
ISBN: 978-3-11-222256-0
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 6 - ePub Watermark

E-Book, Englisch, Band 8, 280 Seiten

Reihe: Global and Comparative Data Law

The author explores the nature of data anonymization under the GDPR, with a particular focus on means of differential privacy. First, he examines the requirement of „identified or identifiable“ in Art. 4 GDPR. Building on this foundation, he describes and evaluates different methods for the anonymization of structured and unstructured data, especially text data. The author describes the role of machine learning and artificial intelligence with regard to anonymizing unstructured data and elaborates on the data protection implications of training and using AI/ML models (including federated learning setups) for this purpose.

Zielgruppe

Researchers, Professionals and Practitioners, Teachers and Instru

Autoren/Hrsg.

Gerlach, Lauritz

Fachgebiete

• Rechtswissenschaften Öffentliches Recht Verwaltungsrecht Allgemeines Informationsrecht, Datenschutzrecht

Über Autor(innen)

Lauritz Gerlach, Hamburg.