Privacy Preserving Learning with Adjustable Utility Privacy Trade-off, Ph.D. Dissertation Sharif University of Technology ; Aref, Mohammad Reza (Supervisor)
Abstract
The rapid evolution of artificial intelligence (AI) technologies has led to the widespread adoption of AI systems in diverse research and industrial fields. Deep neural networks, at the forefront of AI's power, demonstrate high performance by leveraging large volumes of training data. However, acquiring such vast amounts of data requires collaboration among individual data owners, who may have concerns about privacy. To address these concerns, various privacy-preserving methodologies have been proposed. These methodologies share a common goal of striking a balance between preserving privacy and maintaining data utility. This study aims to explore and analyze these privacy protection...
Cataloging briefPrivacy Preserving Learning with Adjustable Utility Privacy Trade-off, Ph.D. Dissertation Sharif University of Technology ; Aref, Mohammad Reza (Supervisor)
Abstract
The rapid evolution of artificial intelligence (AI) technologies has led to the widespread adoption of AI systems in diverse research and industrial fields. Deep neural networks, at the forefront of AI's power, demonstrate high performance by leveraging large volumes of training data. However, acquiring such vast amounts of data requires collaboration among individual data owners, who may have concerns about privacy. To address these concerns, various privacy-preserving methodologies have been proposed. These methodologies share a common goal of striking a balance between preserving privacy and maintaining data utility. This study aims to explore and analyze these privacy protection...
Find in contentBookmark |
|