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cs.CR2020
POSEIDON: Privacy-Preserving Federated Neural Network Learning
Sinem Sav, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza +4
In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an -party, federated learning setting. We propose a novel system, POSEI…
cs.CR2020
Scalable Privacy-Preserving Distributed Learning
David Froelicher, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis +4
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstracti…