TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations

Abstract:The demand for privacy-compliant AI has amplified the need for machine unlearning; yet, existing retraining or distillation-based methods remain unverifiable and computationally costly. We introduce TrustErase, a verifiable, data-free unlearning framework leveraging passport-embedded representations for instant, modular, and auditable forgetting. By treating passports as cryptographic keys within parameter-efficient adaptation layers, TrustErase enables the removal of specific classes or datasets through simple deactivation, without retraining, fine-tuning, or access to the original data. A singular value based decomposition conceals passports within model weights, ensuring that unlearning actions remain transparent and provably compliant. Evaluations on MNIST, CIFAR10 and CIFAR100 show that TrustErase matches or exceeds state-of-the-art benchmarks such as DELETE, L2UL, and Boundary Shrink, while operating in a strictly data-free regime. Ultimately, TrustErase establishes a new paradigm for trustworthy, accountable, and instantly forgettable AI systems.

Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.17122 [cs.CR]

(or arXiv:2606.17122v1 [cs.CR] for this version)

https://doi.org/10.48550/arXiv.2606.17122 Focus to learn more arXiv-issued DOI via DataCite

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