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The paper introduces ExecCert, a release-time certification layer that bridges the gap between mathematical certified machine-unlearning guarantees and the concrete finite-precision artifacts actually deployed, including an incremental Retraining-Reference Release Verification for sequential deletion requests.
Proposes a practical auditor that uses membership inference attacks to compute data-dependent lower bounds on the unlearning parameter, finding a sharp separation between certified algorithms (e.g., model clipping, rewind-to-delete) that achieve tight bounds and empirical methods (e.g., Hessian-based unlearning, gradient ascent) that exhibit large bounds, indicating poor unlearning.