Improving privacy of Federated Learning Generative Adversarial Networks using Intel SGX

Bachelor Thesis (2022)
Author(s)

W. Jehee (TU Delft - Electrical Engineering, Mathematics and Computer Science)

Contributor(s)

Kaitai Liang – Mentor (TU Delft - Cyber Security)

Julián Urbano – Graduation committee member (TU Delft - Multimedia Computing)

R. Wang – Mentor (TU Delft - Cyber Security)

Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright
© 2022 Wouter Jehee
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Wouter Jehee
Graduation Date
22-06-2022
Awarding Institution
Delft University of Technology
Project
CSE3000 Research Project
Programme
Computer Science and Engineering
Faculty
Electrical Engineering, Mathematics and Computer Science
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Abstract

Federated learning (FL), although a major privacy improvement over centralized learning, is still vulnerable to privacy leaks. The research presented in this paper provides an analysis of the threats to FL Generative Adversarial Networks. Furthermore, an implementation is provided to better protect the data of the participants with Trusted Execution Environments (TEEs), using Intel Software Guard Extensions. Lastly, the viability of it’s use in practice is evaluated and discussed. The results indicate that this approach protects the data, while not affecting the predicting capabilities of the model, with a noticeable but manageable impact on the training duration.

Files

Final_paper_cse3000.pdf
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