6 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…
Privacy at Scale in Networked Healthcare
M. Amin Rahimian, Benjamin Panny, James Joshi
Digitized, networked healthcare promises earlier detection, precision therapeutics, and continuous care; yet, it also expands the surface for privacy loss and compliance risk. We a…
PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction
Peilin He, James Joshi
Reconstructing high-quality images from low-resolution inputs using Residual Dense Spatial Networks (RDSNs) is crucial yet challenging. It is even more challenging in centralized t…
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…
Sparsification Under Siege: Dual-Level Defense Against Poisoning in Communication-Efficient Federated Learning
Zhiyong Jin, Runhua Xu, Chao Li +3
Gradient sparsification, while mitigating communication bottlenecks in Federated Learning (FL), fundamentally alters the geometric landscape of model updates. We reveal that the re…
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…