Anonymization and Identifiability
Language: English
Published by De Gruyter, DE, 2026
- Hardcover
- New
Seller: Rarewaves USA, HEBRON, KY, U.S.A.Rarewaves USA
AbeBooks seller since June 10, 2025
Condition: New
£ 81.97
Quantity: Over 20 available
Add to basketItem description from seller
Daten sind eine zentrale Ressource des 21. Jahrhunderts. Daten dienen individuellen, kollektiven und Gemeinwohlzwecken. Daten und Datenökosysteme prägen die Wettbewerbsfähigkeit von kommerziellen, nicht-kommerziellen und staatlichen Akteuren gleichermaßen. Die Kalibrierung der Nutzung und Nutzungsszenarien von Daten ist eine eminente Frage für (Selbst-)Regulierer - auf dem nationalen, supranationalen und internationalen Level. Globale, vergleichende und interdisziplinäre Perspektiven sind notwendig, um eine adäquate Balance zwischen datenbezogener Kooperation und datenbezogenem Wettbewerb zu erzielen. Diese Perspektiven reichen weit über das Datenschutzrecht hinaus und umfassen unter anderem das Daten(wirtschafts)recht, das Open Data-Recht, das Geheimnisschutzrecht und das Immaterialgüterrecht. Vor diesem Hintergrund widmet sich die Reihe zentralen Fragestellungen des Internationalen und Vergleichenden Datenrechts sowie der Datenpolitik. Die Reihe umfasst Studien und Monographien sowie Tagungs- und Sammelbände.…
Seller Inventory # LU-9783119142601
- Title
- Anonymization and Identifiability
- Author
- Lauritz Gerlach
- Publisher
- De Gruyter, DE
- Publication year
- 2026
- Condition
- New
- Binding
- Hardback
- Language
- English
- ISBN 10
- 3119142603
- ISBN 13
- 9783119142601
- Item weight
- 500 grams
- Dimensions
- 15.6 x 1.75 x 23.39 cm
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.
"Synopsis" may belong to another edition of this title.
About the Author
Lauritz Gerlach, Hamburg.
"About the title" may belong to another edition of this title.
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