3 papers
cs.CR2026
ZK-APEX: Zero-Knowledge Approximate Personalized Unlearning with Executable Proofs
Mohammad M Maheri, Sunil Cotterill, Alex Davidson +1
Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers…
cs.LG2025
Verifiable Unlearning on Edge
Mohammad M Maheri, Alex Davidson, Hamed Haddadi
Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infrin…
cs.LG2025
TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks
Mohammad M Maheri, Hamed Haddadi, Alex Davidson
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires acce…