privacy-preserving machine learning

Bayesian Decision-Theoretic Privacy

Christian P. Robert, Full Professor, Department of Applied Mathematics (CEREMADE), Paris Dauphine-PSL University;

Nov 2, 12:00 - 13:00

B3 R5209

Data Privacy statistical privacy bayesian adversial privacy privacy-preserving machine learning privacy-preserving AI bayesian methods

This seminar introduces two Bayesian decision-theoretic frameworks, persuasive privacy and Bayesian adversarial privacy, that quantify statistical privacy by evaluating how data releases influence adversarial beliefs and decisions while offering contextual alternatives to conventional differential privacy.

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.