PROJECTS
Privacy, Fairness and Trustworthy Health AI
This project develops methods for protecting sensitive health data while improving fairness and accountability in AI systems. The research spans ECG re-identification, adaptive anonymization, synthetic data generation, causal fairness, and memorization risks in large language models.
- Linkage Attacks Expose Identity Risks in Public ECG Data Sharing
- REACT: Reinforcement Learning-Based Adaptive ECG Anonymization and Privacy Threat Mitigation
- TransECG: Leveraging Transformers for Explainable ECG Re-identification Risk Analysis
- FairTabGen: Unifying Counterfactual and Causal Fairness in Synthetic Tabular Data Generation
- FairCauseSyn: Towards Causally Fair LLM-Augmented Synthetic Data Generation
- Skewed Memorization in Large Language Models: Quantification and Decomposition
Project information
- Category: Large Language Models and Population Models
- Contact Person: Amir M. Rahmani
