147 citations · 393 across the 14 of their papers we have counts for
14 papers
Comments on "Privacy-Enhanced Federated Learning Against Poisoning Adversaries"
Thomas Schneider, Ajith Suresh, Hossein Yalame
In August 2021, Liu et al. (IEEE TIFS'21) proposed a privacy-enhanced framework named PEFL to efficiently detect poisoning behaviours in Federated Learning (FL) using homomorphic e…
WW-FL: Secure and Private Large-Scale Federated Learning
Felix Marx, Thomas Schneider, Ajith Suresh +3
Federated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices. However, recent…
ScionFL: Efficient and Robust Secure Quantized Aggregation
Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas +7
Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunat…
MPClan: Protocol Suite for Privacy-Conscious Computations
Nishat Koti, Shravani Patil, Arpita Patra +1
The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical s…
High-Throughput Secure Multiparty Computation with an Honest Majority in Various Network Settings
Christopher Harth-Kitzerow, Ajith Suresh, Yongqin Wang +3
In this work, we present novel protocols over rings for semi-honest secure three-party computation (3PC) and malicious four-party computation (4PC) with one corruption. While most…
Privacy-Preserving Epidemiological Modeling on Mobile Graphs
Daniel Günther, Marco Holz, Benjamin Judkewitz +4
The latest pandemic COVID-19 brought governments worldwide to use various containment measures to control its spread, such as contact tracing, social distance regulations, and curf…