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2020 – today
- 2023
[j3]Aidmar Wainakh, Ephraim Zimmer
, Sandeep Subedi, Jens Keim, Tim Grube, Shankar Karuppayah, Alejandro Sánchez Guinea, Max Mühlhäuser:
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups. Sensors 23(1): 31 (2023)- [c10]Ahmad Khalil, Aidmar Wainakh, Ephraim Zimmer, Javier Parra-Arnau, Antonio Fernández Anta, Tobias Meuser, Ralf Steinmetz:
Label-Aware Aggregation for Improved Federated Learning. FMEC 2023: 216-223 - 2022
- [b1]Aidmar Wainakh:
On Privacy-Enhanced Distributed Analytics in Online Social Networks. Technical University of Darmstadt, Germany, 2022 - [j2]Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, Max Mühlhäuser:
User-Level Label Leakage from Gradients in Federated Learning. Proc. Priv. Enhancing Technol. 2022(2): 227-244 (2022) - 2021
- [j1]Carlos Garcia Cordero, Emmanouil Vasilomanolakis, Aidmar Wainakh, Max Mühlhäuser, Simin Nadjm-Tehrani:
On Generating Network Traffic Datasets with Synthetic Attacks for Intrusion Detection. ACM Trans. Priv. Secur. 24(2): 8:1-8:39 (2021) - [c9]Aidmar Wainakh, Aleksej Strassheim, Tim Grube, Jörg Daubert, Max Mühlhäuser:
Enabling Privacy-Preserving Rule Mining in Decentralized Social Networks. ARES 2021: 27:1-27:11 - [c8]Aidmar Wainakh, Till Müßig, Tim Grube, Max Mühlhäuser:
Label Leakage from Gradients in Distributed Machine Learning. CCNC 2021: 1-4 - [c7]Angela Borchert, Aidmar Wainakh, Nicole Krämer, Max Mühlhäuser, Maritta Heisel:
The Relevance of Privacy Concerns, Trust, and Risk for Hybrid Social Media. ENASE (Selected Papers) 2021: 88-111 - [c6]Angela Borchert, Aidmar Wainakh, Nicole Krämer, Max Mühlhäuser, Maritta Heisel:
Mitigating Privacy Concerns by Developing Trust-related Software Features for a Hybrid Social Media Application. ENASE 2021: 269-280 - [i3]Aidmar Wainakh, Fabrizio Ventola, Till Müßig, Jens Keim, Carlos Garcia Cordero, Ephraim Zimmer, Tim Grube, Kristian Kersting, Max Mühlhäuser:
User Label Leakage from Gradients in Federated Learning. CoRR abs/2105.09369 (2021) - 2020
- [c5]Aidmar Wainakh, Alejandro Sánchez Guinea, Tim Grube, Max Mühlhäuser:
Enhancing Privacy via Hierarchical Federated Learning. EuroS&P Workshops 2020: 344-347 - [i2]Aidmar Wainakh, Alejandro Sánchez Guinea, Tim Grube, Max Mühlhäuser:
Enhancing Privacy via Hierarchical Federated Learning. CoRR abs/2004.11361 (2020)
2010 – 2019
- 2019
- [c4]Aidmar Wainakh, Tim Grube, Jörg Daubert, Carsten Porth, Max Mühlhäuser:
Tweet beyond the Cage: A Hybrid Solution for the Privacy Dilemma in Online Social Networks. GLOBECOM 2019: 1-6 - [c3]Aidmar Wainakh, Tim Grube, Jörg Daubert, Max Mühlhäuser:
Efficient privacy-preserving recommendations based on social graphs. RecSys 2019: 78-86 - [i1]Carlos Garcia Cordero, Emmanouil Vasilomanolakis, Aidmar Wainakh, Max Mühlhäuser, Simin Nadjm-Tehrani:
On generating network traffic datasets with synthetic attacks for intrusion detection. CoRR abs/1905.00304 (2019) - 2017
- [c2]Dirk Siegmund, Aidmar Wainakh, Tina Ebert, Andreas Braun, Arjan Kuijper:
Text Localization in Born-Digital Images of Advertisements. CIARP 2017: 627-634 - 2016
- [c1]Dirk Siegmund, Biying Fu, Timotheos Samartzidis, Aidmar Wainakh, Arjan Kuijper, A. Braun:
Attack detection in an autonomous entrance system using optical flow. ICDP 2016: 1-6
Coauthor Index
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