Federated Learning in Autonomous Vehicles Setting- GDPR perspective
(2022) JAEM03 20211Department of Law
Faculty of Law
- Abstract (Swedish)
- This paper legally tests federated learning as a machine learning technique, which guarantees a high level of privacy. This paper provides answers to all legal aspects of this technical development as the first legal paper in this area, while challenging vague GDPR requirements. This method showed its supremacy over conventional anonymization techniques and other machine learning techniques. It might be an all-mighty solution for all the future technical developments that pledge GDPR compliance.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9100042
- author
- Bogucanin Volic, Seada LU
- supervisor
-
- Ana Nordberg LU
- organization
- alternative title
- Federated Learning- GDPR perspective
- course
- JAEM03 20211
- year
- 2022
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Law and technology, Artificial Intelligence, Privacy, Machine learning, Privacy protection, Privacy preservation techniques, Compliance, Autonomous vehicles
- language
- English
- id
- 9100042
- date added to LUP
- 2022-09-27 11:06:46
- date last changed
- 2022-09-27 11:06:46
@misc{9100042, abstract = {{This paper legally tests federated learning as a machine learning technique, which guarantees a high level of privacy. This paper provides answers to all legal aspects of this technical development as the first legal paper in this area, while challenging vague GDPR requirements. This method showed its supremacy over conventional anonymization techniques and other machine learning techniques. It might be an all-mighty solution for all the future technical developments that pledge GDPR compliance.}}, author = {{Bogucanin Volic, Seada}}, language = {{eng}}, note = {{Student Paper}}, title = {{Federated Learning in Autonomous Vehicles Setting- GDPR perspective}}, year = {{2022}}, }