3 citations · 5 across the 10 of their papers we have counts for
5 papers · 1 filter
Auditing Approximate Machine Unlearning for Differentially Private Models
Yuechun Gu, Jiajie He, Keke Chen
Approximate machine unlearning aims to remove the effect of specific data from trained models to ensure individuals' privacy. Existing methods focus on the removed records and assu…
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…
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…
Adaptive Domain Inference Attack with Concept Hierarchy
Yuechun Gu, Jiajie He, Keke Chen
With increasingly deployed deep neural networks in sensitive application domains, such as healthcare and security, it's essential to understand what kind of sensitive information c…
GAN-based Domain Inference Attack
Yuechun Gu, Keke Chen
Model-based attacks can infer training data information from deep neural network models. These attacks heavily depend on the attacker's knowledge of the application domain, e.g., u…