%0 Conference Proceedings
%A Stach, Christoph; Giebler, Corinna; Wagner, Manuela; Weber, Christian & Mitschang, Bernhard
%D 2020
%T AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning
%E Furnell, Steven; Mori, Paolo; Weippl, Edgar & Camp, Olivier
%B Proceedings of the 6th International Conference on Information Systems Security and Privacy
%C Valletta
%I SciTePress
%V 1
%P 21-32
%S ICISSP '20
%8 February
%@ 978-989-758-399-5
%3 inproceedings
%F icissp_20_amnesia
%K machine learning; data protection; privacy zones; access control; model management; provenance; GDPR
%X Machine Learning (ML) applications are becoming increasingly valuable due to the rise of IoT technologies. That is, sensors continuously gather data from different domains and make them available to ML for learning its models. This provides profound insights into the data and enables predictions about future trends. While ML has many advantages, it also represents an immense privacy risk. Data protection regulations such as the GDPR address such privacy concerns, but practical solutions for the technical enforcement of these laws are also required. Therefore, we introduce AMNESIA, a privacy-aware machine learning model provisioning platform. AMNESIA is a holistic approach covering all stages from data acquisition to model provisioning. This enables to control which application may use which data for ML as well as to make models "forget" certain knowledge.
%R 10.5220/0008916700210032