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.
%0 Conference Paper
%1 icissp_20_amnesia
%A Stach, Christoph
%A Giebler, Corinna
%A Wagner, Manuela
%A Weber, Christian
%A Mitschang, Bernhard
%B Proceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacy
%C Valletta
%D 2020
%E Furnell, Steven
%E Mori, Paolo
%E Weippl, Edgar
%E Camp, Olivier
%I SciTePress
%K GDPR access_control data_protection machine_learning model_management privacy_zones provenance
%P 21–32
%R 10.5220/0008916700210032
%T AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning
%V 1
%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.
%@ 978-989-758-399-5
@inproceedings{icissp_20_amnesia,
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.},
added-at = {2020-09-21T11:45:55.000+0200},
address = {Valletta},
author = {Stach, Christoph and Giebler, Corinna and Wagner, Manuela and Weber, Christian and Mitschang, Bernhard},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/25eef471a46a231ca496ce22e610b8781/christophstach},
booktitle = {Proceedings of the 6ᵗʰ International Conference on Information Systems Security and Privacy},
doi = {10.5220/0008916700210032},
editor = {Furnell, Steven and Mori, Paolo and Weippl, Edgar and Camp, Olivier},
interhash = {4f6d8079e3b0e4cde537b9ede9972e6d},
intrahash = {5eef471a46a231ca496ce22e610b8781},
isbn = {978-989-758-399-5},
keywords = {GDPR access_control data_protection machine_learning model_management privacy_zones provenance},
month = feb,
pages = {21–32},
publisher = {SciTePress},
series = {ICISSP '20},
timestamp = {2020-09-21T09:45:55.000+0200},
title = {AMNESIA: A Technical Solution towards GDPR-compliant Machine Learning},
volume = 1,
year = 2020
}
