3 papers
cs.LG2026
WHERE to Generate Matters: Budget-Aware Synthetic Augmentation for Label Skewed Federated Learning
Sangwoo Lee, Sunghwan Park, Jaewoo Lee
Label skew in federated learning (FL) causes client drift and degrades global accuracy. Synthetic data augmentation can reduce this imbalance; however, full class balancing require…
cs.CR2026
REAN: Reconstruction-aware ECG Anonymization Based on Privacy--Utility Orthogonality
Taerin Ki, Sunghwan Park, Junyoung Park +1
A shared electrocardiogram (ECG) is itself a biometric fingerprint that can re-identify a patient and reveal personal information. Recent ECG anonymizers transform the signal befor…
cs.CV2026
Unlearnable Faces: Privacy Protection Surviving Extraction Pipeline
Byunghoon Oh, Sunghwan Park, Jaewoo Lee
Unlearnable examples keep publicly shared photos from being learned by unauthorized face-recognition models. An imperceptible perturbation, added before sharing, makes any model tr…