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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Understanding and Mitigating Privacy Vulnerabilities in Deep Learning
Committee:
Dr. Moinuddin Qureshi, CoC, Chair, Advisor
Dr. , Co-Advisor
Dr. Ghassan AlRegib, ECE
Dr. Tushar Krishna, ECE
Dr. Atul Prakash, U Michigan
Dr. Edward Suh, Cornell
Abstract: This thesis demonstrates that the information exchanged during training and inference can compromise data and model privacy by discovering the vulnerabilities of frameworks used for privacy-preserving training and inference. We leverage these vulnerabilities to develop new attacks that overcome the limitations of prior works and achieve state-of-the-art attack performance, breaking the illusion of privacy and emphasizing the need for principled defenses. Furthermore, our insights have led to the development of defenses that safeguard model privacy with minimal impact on performance, enabling the practical deployment of models for remote inference while protecting privacy.
