4 papers
Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access
Liou Tang, James Joshi, Ashish Kundu
Quantum Machine Learning (QML) has shown rapid advances by utilizing quantum computing for machine learning tasks. Meanwhile, the privacy risks accompanying QML is also starting to…
Characterizing Privacy-Audit Alignment in Behavioral Audit of Machine Unlearning
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 is no agreed-upon scheme for auditing MU. Existing…
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…
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…