Area of research
Artificial Intelligence · Sociology and Political Science
Research interest
Research interests include Privacy-Preserving Technologies in Data, Adversarial Robustness in Machine Learning, Privacy, Security, and Data Protection, and Internet Traffic Analysis and Secure E-voting.
The Data Minimization Principle in Machine Learning
The Data Minimization Principle in Machine Learning
On The Impact of Machine Learning Randomness on Group Fairness
Enhanced Membership Inference Attacks against Machine Learning Models
What Does it Mean for a Language Model to Preserve Privacy?
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks
Data Privacy and Trustworthy Machine Learning
On the Privacy Risks of Algorithmic Fairness
Bypassing Backdoor Detection Algorithms in Deep Learning
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
Membership Inference Attacks Against Adversarially Robust Deep Learning Models
Machine Learning with Membership Privacy using Adversarial Regularization
Membership Inference Attacks Against Machine Learning Models
Plausible deniability for privacy-preserving data synthesis
Privacy Games Along Location Traces
Privacy-Preserving Deep Learning
Privacy-preserving deep learning
Protecting location privacy