TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
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 unlearnin...
| Authors | Rutger Hendrix, Leonardo G. Russo, Concetto Spampinato et al. |
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| Primary category | cs.CR |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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