3 papers
cs.LG2026
Behavioral Audit of Machine Unlearning Has a Privacy Cost
Liou Tang, James Joshi, Ashish Kundu
The removal of learned data from Machine Learning models through Machine Unlearning (MU) has been widely studied; however, there has yet to be an agreed-upon scheme for auditing MU…
cs.CR2026
Conformal-DP: A Density-Aware Mechanism for Differential Privacy over Riemannian Manifolds via Conformal Transformation
Peilin He, Liou Tang, M. Amin Rahimian +1
Differential Privacy (DP) is being increasingly adopted for non-Euclidean data that lie on complex, high-dimensional manifolds. Existing DP mechanisms for manifold data consider ge…
cs.LG2025
Apollo: A Posteriori Label-Only Membership Inference Attack Towards Machine Unlearning
Liou Tang, James Joshi, Ashish Kundu
Machine Unlearning (MU) aims to update Machine Learning (ML) models following requests to remove training samples and their influences on a trained model efficiently without retrai…