77 citations · 152 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Adaptive ABAC Policy Learning: A Reinforcement Learning Approach
Leila Karimi, Mai Abdelhakim, James Joshi
With rapid advances in computing systems, there is an increasing demand for more effective and efficient access control (AC) approaches. Recently, Attribute Based Access Control (A…
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…
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…