Skip to main content
King Abdullah University of Science and Technology
Communication Theory Lab
Communication Theory Lab
  • Home
  • News
  • Events
  • People
    • All People
    • Principal Investigator
    • Research Scientists
    • Postdoctoral Fellows
    • Students
    • Visiting Scholars
    • Former Members
    • Former Members from Texas A&M University
    • Former Members from University of Minnesota
    • Collaborators
    • Alumni
  • Research
  • Publications
  • Teaching
  • Funding
  • Media
  • Contact Us

privacy-preserving machine learning

KAUST-CEMSE-CS-PhD-Dissertation-Defense-Lijie-Zihang Xiang-Modern-Privacy-preserving-Machine-Learning

Modern Privacy-preserving Machine Learning: Rigorous Approach for Data Privacy

Zihang Xiang, Ph.D. Student, Computer Science
Jul 6, 10:00 - 12:00

B3 L5 R5216

privacy-preserving machine learning Differential privacy Federated learning

This dissertation centers around privacy-preserving technologies (differential privacy) in broad machine learning applications. This dissertation focuses on two sides of differential privacy: 1) designing privacy-preserving algorithms, 2) ensuring the falsifiability of privacy claims.

Communication Theory Lab (CTL)

Footer

  • A-Z Directory
    • All Content
    • Browse Related Sites
  • Site Management
    • Log in

© 2025 King Abdullah University of Science and Technology. All rights reserved. Privacy Notice

Disclaimer: The views and opinions expressed in this page are strictly those of the page author. The contents of this page have not been reviewed or approved by the King Abdullah University of Science and Technology.