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cs.LG2024
Calibrating Practical Privacy Risks for Differentially Private Machine Learning
Yuechun Gu, Keke Chen
Differential privacy quantifies privacy through the privacy budget , yet its practical interpretation is complicated by variations across models and datasets. Recent research on…
cs.LG2024
FT-PrivacyScore: Personalized Privacy Scoring Service for Machine Learning Participation
Yuechun Gu, Jiajie He, Keke Chen
Training data privacy has been a top concern in AI modeling. While methods like differentiated private learning allow data contributors to quantify acceptable privacy loss, model u…