302 citations · 547 across the 18 of their papers we have counts for
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cs.LG2021★ 77 cited
Privacy-Preserving Machine Learning: Methods, Challenges and Directions
Runhua Xu, Nathalie Baracaldo, James Joshi
Machine learning (ML) is increasingly being adopted in a wide variety of application domains. Usually, a well-performing ML model relies on a large volume of training data and high…
cs.LG2021★ 16 cited
FedV: Privacy-Preserving Federated Learning over Vertically Partitioned Data
Runhua Xu, Nathalie Baracaldo, Yi Zhou +3
Federated learning (FL) has been proposed to allow collaborative training of machine learning (ML) models among multiple parties where each party can keep its data private. In this…
cs.LG2020★ 10 cited
NN-EMD: Efficiently Training Neural Networks using Encrypted Multi-Sourced Datasets
Runhua Xu, James Joshi, Chao Li
Training a machine learning model over an encrypted dataset is an existing promising approach to address the privacy-preserving machine learning task, however, it is extremely chal…