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pro vyhledávání: '"Wainakh, Aidmar"'
Autor:
Wainakh, Aidmar, Zimmer, Ephraim, Subedi, Sandeep, Keim, Jens, Grube, Tim, Karuppayah, Shankar, Guinea, Alejandro Sanchez, Mühlhäuser, Max
Federated learning (FL) enables a set of entities to collaboratively train a machine learning model without sharing their sensitive data, thus, mitigating some privacy concerns. However, an increasing number of works in the literature propose attacks
Externí odkaz:
http://arxiv.org/abs/2111.03363
Autor:
Wainakh, Aidmar, Ventola, Fabrizio, Müßig, Till, Keim, Jens, Cordero, Carlos Garcia, Zimmer, Ephraim, Grube, Tim, Kersting, Kristian, Mühlhäuser, Max
Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the
Externí odkaz:
http://arxiv.org/abs/2105.09369
Federated learning suffers from several privacy-related issues that expose the participants to various threats. A number of these issues are aggravated by the centralized architecture of federated learning. In this paper, we discuss applying federate
Externí odkaz:
http://arxiv.org/abs/2004.11361
Autor:
Cordero, Carlos Garcia, Vasilomanolakis, Emmanouil, Wainakh, Aidmar, Mühlhäuser, Max, Nadjm-Tehrani, Simin
Most research in the area of intrusion detection requires datasets to develop, evaluate or compare systems in one way or another. In this field, however, finding suitable datasets is a challenge on to itself. Most publicly available datasets have neg
Externí odkaz:
http://arxiv.org/abs/1905.00304
Autor:
Wainakh, Aidmar1 (AUTHOR) wainakh@tk.tu-darmstadt.de, Zimmer, Ephraim1 (AUTHOR) wainakh@tk.tu-darmstadt.de, Subedi, Sandeep1 (AUTHOR), Keim, Jens1 (AUTHOR), Grube, Tim1 (AUTHOR), Karuppayah, Shankar2 (AUTHOR), Sanchez Guinea, Alejandro1 (AUTHOR), Mühlhäuser, Max1 (AUTHOR)
Publikováno v:
Sensors (14248220). Jan2023, Vol. 23 Issue 1, p31. 33p.
Autor:
Wainakh, Aidmar, Ventola, Fabrizio, Müßig, Till, Keim, Jens, Cordero, Carlos Garcia, Zimmer, Ephraim, Grube, Tim, Kersting, Kristian, Mühlhäuser, Max
Publikováno v:
Proceedings on Privacy Enhancing Technologies. 2022:227-244
Federated learning enables multiple users to build a joint model by sharing their model updates (gradients), while their raw data remains local on their devices. In contrast to the common belief that this provides privacy benefits, we here add to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77f817d1d45c0c113cb02dc11f85327e
https://doi.org/10.2478/popets-2022-0043
https://doi.org/10.2478/popets-2022-0043
Autor:
Wainakh, Aidmar
More than half of the world's population benefits from online social network (OSN) services. A considerable part of these services is mainly based on applying analytics on user data to infer their preferences and enrich their experience accordingly.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::284b35357540341ce8c166ef3775fb52
Akademický článek
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Autor:
GARCIA CORDERO, CARLOS, VASILOMANOLAKIS, EMMANOUIL, WAINAKH, AIDMAR, MÜHLHÄUSER, MAX, NADJM-TEHRANI, SIMIN
Publikováno v:
ACM Transactions on Privacy & Security; Feb2021, Vol. 24 Issue 2, p1-39, 39p
Autor:
Wainakh A; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany., Zimmer E; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany., Subedi S; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany., Keim J; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany., Grube T; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany., Karuppayah S; National Advanced IPv6 Centre (NAv6), University of Science Malaysia, Penang 11800, Malaysia., Sanchez Guinea A; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany., Mühlhäuser M; Telecooperation Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany.
Publikováno v:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Dec 20; Vol. 23 (1). Date of Electronic Publication: 2022 Dec 20.