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Privacy-preserving distributed deep learning
Prashar, Aseem
Prashar, Aseem
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2020-05
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Electronic dissertations
Electronic dissertations
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Abstract
Deep neural networks are becoming popular in a variety of fields due to their ability
to learn from large-scale data sets. Recently, researchers have proposed distributed learning
architectures that allow multiple users to share their data to train deep learning models.
Unfortunately, privacy and confidentiality concerns limit the application of this approach,
preventing certain organizations such as medical institutions to fully benefit from
distributed deep learning. To overcome this challenge, researches have proposed algorithms
that only share neural network parameters. This approach allows users to keep their
private datasets secret while still having access to the improved deep neural networks
trained with the data from all participants. However, existing distributed learning
approaches are vulnerable to attacks where a malicious user can use the the shared neural
network parameters to recreate the private data from other users.
We propose a distributed deep learning algorithm that allows a user to improve its
deep-learning model while preserving its privacy from such attacks. Specifically, our
approach focuses on protecting the privacy of a single user by limiting the number of times
other users can download and upload parameters from the main deep neural network. By
doing so, our approach limits ability of the attackers to recreate private data samples from
the reference user while maintaining a highly accurate deep neural network.
Our approach is flexible and can be adapted to work with any deep neural network
architectures. We conduct extensive experiments to verify the proposed approach. We
observe that the trained neural network can achieve an accuracy of 95.18%, while
protecting the privacy of the reference user by preventing it from sharing both its private
data and deep neural network parameters with the server or other users.
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Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Wichita State University
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Copyright 2020 by Aseem Prashar
All Rights Reserved