ZK-APEX: ZERO-KNOWLEDGE APPROXIMATE PERSONALIZED UNLEARNING WITH EXECUTABLE PROOFS

Machine unlearning removes the influence of specified data from trained models to satisfy privacy, copyright, and safety requirements (e.g., the “right to be forgotten”). In practice, providers distribute a global model to edge devices, that each locally personalize the model based on their private data. However, since clients may ignore or falsify deletion requests, providers must verify correct unlearning for these distributed models, without accessing private parameters. This is particularly challenging for personalized models, which must forget designated samples without degrading local utility, while ensuring that verification remains efficient and scalable on resource-constrained edge devices. We formalize personalized unlearning and develop a zero-shot approximate unlearning algorithm that works directly on the personalized model without retraining. Our novel method, \name, combines provider-side sparse masking for targeted removal with client-side Group-OBS compensation computed from a block-wise empirical Fisher. This technique yields a curvature-aware update designed for low-overhead execution and proof generation. Using modern Halo2 ZK-SNARKs, we prove operator compliance by showing that the unlearned model exactly matches the committed output of the prescribed transformation, without revealing personalized model parameters or data. On Vision Transformer (ViT) classification models, our approach recovers approximately 99\% Top-1 personalization accuracy while enforcing effective forgetting. We further evaluate the unlearning algorithm on a generative model, OPT125M, trained on the CodeParrot code dataset, achieving $\sim$70\% recovery of original accuracy. ZK-SNARK proof generation for the ViT case completes in $\approx$2~hours, which is more than $10^7\times$ faster than retraining based verification, with peak memory under 0.7~GB and proof sizes about 400~MB. Together, these results establish the first verifiable personalized unlearning framework practical for deployment on resource constrained edge devices.