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.

翻译：隐私合规人工智能的需求放大了机器遗忘的必要性；然而，现有的基于重训练或蒸馏的方法仍难以被验证且计算成本高昂。我们提出TrustErase——一种可验证、无数据的遗忘框架，利用护照嵌入表示实现即时、模块化且可审计的遗忘。通过将护照作为参数高效适配层内的加密密钥，TrustErase能够通过简单的停用操作移除特定类别或数据集，无需重训练、微调或访问原始数据。基于奇异值的分解技术将护照隐藏于模型权重中，确保遗忘行为透明且可证明合规。在MNIST、CIFAR10和CIFAR100上的评估表明，TrustErase在严格无数据条件下达到或超越了DELETE、L2UL及Boundary Shrink等现有最优基准。最终，TrustErase为可信、可问责且即时可遗忘的AI系统确立了新范式。