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Federated Learning in Autonomous Vehicles Setting- GDPR perspective

Bogucanin Volic, Seada LU (2022) JAEM03 20211
Department 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.
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author
Bogucanin Volic, Seada LU
supervisor
organization
alternative title
Federated Learning- GDPR perspective
course
JAEM03 20211
year
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}},
}