TY  - CONF
AU  - Stach, Christoph
AU  - Giebler, Corinna
AU  - Wagner, Manuela
AU  - Weber, Christian
AU  - Mitschang, Bernhard
A2  - Furnell, Steven
A2  - Mori, Paolo
A2  - Weippl, Edgar
A2  - Camp, Olivier
T1  - AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning
T2  - Proceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacy
PB  - SciTePress
AD  - Valletta
Y1  - 2020/february
VL  - 1
SP  - 21
EP  - 32
M3  - https://doi.org/10.5220/0008916700210032
KW  - machine learning; data protection; privacy zones; access control; model management; provenance; GDPR
N2  - 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.
ER  -