Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Franziska Boenisch"'
Autor:
Tabea Kossen, Manuel A. Hirzel, Vince I. Madai, Franziska Boenisch, Anja Hennemuth, Kristian Hildebrand, Sebastian Pokutta, Kartikey Sharma, Adam Hilbert, Jan Sobesky, Ivana Galinovic, Ahmed A. Khalil, Jochen B. Fiebach, Dietmar Frey
Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been show
Externí odkaz:
https://doaj.org/article/427bb9acfb874fcfad4b7c1e7629490b
Autor:
Franziska Boenisch
Publikováno v:
Frontiers in Big Data, Vol 4 (2021)
Machine learning (ML) models are applied in an increasing variety of domains. The availability of large amounts of data and computational resources encourages the development of ever more complex and valuable models. These models are considered the i
Externí odkaz:
https://doaj.org/article/429416b4de194c82b7b315fde8dd04b0
Autor:
Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David Dormagen, Fernando Wario, Tim Landgraf
Publikováno v:
Frontiers in Robotics and AI, Vol 5 (2018)
Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for
Externí odkaz:
https://doaj.org/article/83dbebeab9ca47c0a41c0e17c18ee66d
Synthetic data is often presented as a method for sharing sensitive information in a privacy-preserving manner by reproducing the global statistical properties of the original data without dis closing sensitive information about any individual. In pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3d5b9dbf2788e4981194ad9edc675483
http://arxiv.org/abs/2211.10459
http://arxiv.org/abs/2211.10459
Autor:
Franziska Boenisch
Publikováno v:
Datenschutz und Datensicherheit - DuD. 45:448-452
Wir alle generieren taglich grose Mengen an potenziell sensiblen Daten: Worter, die wir auf unseren Smartphones eingeben, Produkte, die wir online kaufen, Gesundheitsdaten, die wir in Apps erfassen. All diese Daten haben eins gemeinsam – sie werden
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faa372cc143a1113f69cbd0cd101ecea
https://doi.org/10.1007/s11623-021-1469-3
https://doi.org/10.1007/s11623-021-1469-3
Autor:
Christiane Kuhn, Marcel Tiepelt, Simon Hanisch, Franziska Boenisch, Paul Francis, Reinhard Munz
Publikováno v:
CCS
A longstanding problem in computer privacy is that of data anonymization. One common approach is to present a query interface to analysts, and anonymize on a query-by-query basis. In practice, this approach often uses a standard database back end, an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::96b375157c3593990617f0fa85bfbd47
https://doi.org/10.1145/3460120.3484751
https://doi.org/10.1145/3460120.3484751
Publikováno v:
Mensch und Computer
Machine learning (ML) models have become increasingly important components of many software systems. Therefore, ensuring their privacy and security is a crucial task. Current research mainly focuses on the development of security and privacy methods.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e2a0db1275568b35b87b9d3e389b3aa
https://doi.org/10.1145/3473856.3473869
https://doi.org/10.1145/3473856.3473869