Privacy-Enhancing Technologies et machine learning
Abstrak
This article examines the main privacy-enhancing technologies (PETs) applied to machine learning – anonymisation, pseudonymisation, differential privacy, federated learning, and homomorphic encryption – and assesses their impact on data protection. By combining a technical approach with legal analysis, it shows how these tools can support compliance with the Swiss DPA and the GDPR, particularly through the principle of data protection by design. While it remains difficult to provide definitive answers de lege lata, the study highlights concrete solutions available to data controllers, while also raising key debates on the legal framework needed to govern the development of AI.
Penulis (1)
Iago Baumann
Akses Cepat
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Cek di sumber asli →- Tahun Terbit
- 2025
- Bahasa
- en
- Sumber Database
- CrossRef
- DOI
- 10.3256/978-3-03929-084-0_02
- Akses
- Terbatas