3 papers
cs.LG2026
Learnability and Privacy Vulnerability are Entangled in a Few Critical Weights
Xingli Fang, Jung-Eun Kim
Prior approaches for membership privacy preservation usually update or retrain all weights in neural networks, which is costly and can lead to unnecessary utility loss or even more…
cs.LG2026
Decoupling Generalizability and Membership Privacy Risks in Neural Networks
Xingli Fang, Jung-Eun Kim
A deep learning model usually has to sacrifice some utilities when it acquires some other abilities or characteristics. Privacy preservation has such trade-off relationships with u…
cs.LG2024
RESQUE: Quantifying Estimator to Task and Distribution Shift for Sustainable Model Reusability
Vishwesh Sangarya, Jung-Eun Kim
As a strategy for sustainability of deep learning, reusing an existing model by retraining it rather than training a new model from scratch is critical. In this paper, we propose R…