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pro vyhledávání: '"Demmler, Daniel"'
Autor:
McDougall, Johanna Ansohn, Burkert, Christian, Demmler, Daniel, Schwarz, Monina, Hubbe, Vincent, Federrath, Hannes
Probe requests help mobile devices discover active Wi-Fi networks. They often contain a multitude of data that can be used to identify and track devices and thereby their users. The past years have been a cat-and-mouse game of improving fingerprintin
Externí odkaz:
http://arxiv.org/abs/2206.03745
Publikováno v:
Proceedings of the 20th International Conference on Security and Cryptography SECRYPT (2023) 312-323
This work investigates and evaluates multiple defense strategies against property inference attacks (PIAs), a privacy attack against machine learning models. Given a trained machine learning model, PIAs aim to extract statistical properties of its un
Externí odkaz:
http://arxiv.org/abs/2205.08821
Autor:
Demmler, Daniel
Protecting users' privacy in digital systems becomes more complex and challenging over time, as the amount of stored and exchanged data grows steadily and systems become increasingly involved and connected. Two techniques that try to approach this is
Autor:
Cammarota, Rosario, Schunter, Matthias, Rajan, Anand, Boemer, Fabian, Kiss, Ágnes, Treiber, Amos, Weinert, Christian, Schneider, Thomas, Stapf, Emmanuel, Sadeghi, Ahmad-Reza, Demmler, Daniel, Stock, Joshua, Chen, Huili, Hussain, Siam Umar, Riazi, Sadegh, Koushanfar, Farinaz, Gupta, Saransh, Rosing, Tajan Simunic, Chaudhuri, Kamalika, Nejatollahi, Hamid, Dutt, Nikil, Imani, Mohsen, Laine, Kim, Dubey, Anuj, Aysu, Aydin, Hosseini, Fateme Sadat, Yang, Chengmo, Wallace, Eric, Norton, Pamela
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems provide customers with timely, informed, and customized inferences to
Externí odkaz:
http://arxiv.org/abs/2008.04449
Akademický článek
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This work investigates and evaluates multiple defense strategies against property inference attacks (PIAs), a privacy attack against machine learning models. Given a trained machine learning model, PIAs aim to extract statistical properties of its un
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4988e2805acfc9ed4fa193e83dc54194
Autor:
Neumann, Carolin, Arlinghaus, Clarissa Sabrina, Demmler, Daniel, Krupka, Daniel, Federrath, Hannes
Die STEM GIrls Eventreihe der Jungen Gesellschaft für Informatik dient der Nachwuchsförderung von zukünftigen STEM Frauen* und wurde 2022 erstmals durchgeführt. Die Reihe besteht aus neun digitalen Workshops für weibliche und non-binäre Persone
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29890b3a0645e29aeabed7fb60f2f12a
https://pub.uni-bielefeld.de/record/2967904
https://pub.uni-bielefeld.de/record/2967904
Autor:
Demmler, Daniel
Publikováno v:
IT: Information Technology; 2022, Vol. 64 Issue 1/2, p49-53, 5p
Publikováno v:
ACM International Conference Proceeding Series; 8/25/2020, p1-10, 10p
Publikováno v:
Proceedings of the 2016 workshop on Applied Networking Research Workshop-ANRW 16
ANRW
ANRW
The growing relevance of Internet eXchange Points (IXPs), where an increasing number of networks exchange routing information, poses fundamental questions regarding the privacy guarantees of confidential business information. To facilitate the exchan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6112c571595e7eae3b4ae963a5182eca