5 papers
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…
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…
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…
Persona Attack: Incremental Memory Injection Jailbreak Attack against Large Language Models
Junyoung Park, Seongyong Ju, Sunghwan Park +1
As Large Language Models evolve for user convenience, vulnerability to jailbreak attacks continues to be reported despite ongoing efforts in safety training. Traditional jailbreak…
Beyond Attack Success Rate: Temporal Logit Observability for LLM Safety Failures
Junyoung Park, Sunghwan Park, Seongyong Ju +1
Attack Success Rate (ASR) evaluates each jailbreak with a single yes/no label at the end of generation, telling us whether a failure happened but not how it unfolded. Two attacks t…