6 papers
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…
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…
Trustworthy AI: Safety, Bias, and Privacy -- A Survey
Xingli Fang, Jianwei Li, Varun Mulchandani +1
The capabilities of artificial intelligence systems have been advancing to a great extent, but these systems still struggle with failure modes, vulnerabilities, and biases. In this…
Representation Magnitude has a Liability to Privacy Vulnerability
Xingli Fang, Jung-Eun Kim
The privacy-preserving approaches to machine learning (ML) models have made substantial progress in recent years. However, it is still opaque in which circumstances and conditions…
Center-Based Relaxed Learning Against Membership Inference Attacks
Xingli Fang, Jung-Eun Kim
Membership inference attacks (MIAs) are currently considered one of the main privacy attack strategies, and their defense mechanisms have also been extensively explored. However, t…
Cooperative Learning for Cost-Adaptive Inference
Xingli Fang, Richard Bradford, Jung-Eun Kim
We propose a cooperative training framework for deep neural network architectures that enables the runtime network depths to change to satisfy dynamic computing resource requiremen…