PrivEdge: From Local to Distributed Private Training and Prediction
Abstract
Machine Learning as a Service (MLaaS) operators provide model training and prediction on the cloud. MLaaS applications often rely on centralised collection and aggregation of user data, which could lead to significant privacy concerns when dealing with sensitive personal data. To address this problem, we propose PrivEdge, a technique for privacy-preserving MLaaS that safeguards the privacy of users who provide their data for training, as well as...
Paper Details
Title
PrivEdge: From Local to Distributed Private Training and Prediction
Published Date
Jan 1, 2020
Pages
1 - 1
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