AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning

Christoph Stach, Corinna Giebler, Manuela Wagner, Christian Weber, Bernhard Mitschang

Abstract

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.

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Paper Citation


in Harvard Style

Stach C., Giebler C., Wagner M., Weber C. and Mitschang B. (2020). AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning.In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-399-5, pages 21-32. DOI: 10.5220/0008916700210032


in Bibtex Style

@conference{icissp20,
author={Christoph Stach and Corinna Giebler and Manuela Wagner and Christian Weber and Bernhard Mitschang},
title={AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2020},
pages={21-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008916700210032},
isbn={978-989-758-399-5},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,
TI - AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning
SN - 978-989-758-399-5
AU - Stach C.
AU - Giebler C.
AU - Wagner M.
AU - Weber C.
AU - Mitschang B.
PY - 2020
SP - 21
EP - 32
DO - 10.5220/0008916700210032