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Bibliographic Details

Title: Anonymization and Identifiability: Enhancing Data Protection Through Differential Privacy and Artificial Intelligence (Global and Comparative Data Law, 8)
Publisher: De Gruyter
Publication Date: 2026
Language: English
ISBN 10: 3119142603
ISBN 13: 9783119142601
Binding: Hardcover
Condition: good

About this title

Synopsis

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.

About the Author

Lauritz Gerlach, Hamburg.

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Big River Books, LLC
5001 McNeel Industrial Blvd, Powder Springs, GA, 30127, U.S.A.

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