用零知识证明实现边缘设备数据可验证删除，保护隐私且不降性能。
Verifiable Unlearning on Edge
- 基于zk-SNARKs构建可验证删除框架，无需暴露数据即可证明已删
- 算法优化使证明生成开销小，适合资源受限的边缘设备
- 保留个性化模型性能，删除后效果几乎无损，适合合规与版权场景
机器学习服务商常将全局模型分发至边缘设备，设备再用本地数据进行个性化。但版权侵权、偏见或监管要求可能需在所有设备上可验证地移除特定数据样本。确保边缘设备正确执行此类删除操作对系统完整性至关重要。本文提出一种基于零知识证明（zk-SNARKs）的验证框架，可在不泄露隐私的前提下，确认个性化边缘模型中的数据已被正确删除。我们设计了专用于高效生成zk-SNARK证明的算法，保证计算与内存开销极低，适用于资源受限的边缘环境。同时，该方法谨慎保留设备端的个性化优化，确保删除后模型性能几乎不受影响。实验结果表明，该框架在实际应用中具备可行性与有效性，实现了可验证、隐私保护且高效的机器学习数据删除。
原文摘要 · Abstract (English)
Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edge devices. Ensuring that edge devices correctly execute such unlearning operations is critical to maintaining integrity. In this work, we introduce a verification framework leveraging zero-knowledge proofs, specifically zk-SNARKs, to confirm data unlearning on personalized edge-device models without compromising privacy. We have developed algorithms explicitly designed to facilitate unlearning operations that are compatible with efficient zk-SNARK proof generation, ensuring minimal computational and memory overhead suitable for constrained edge environments. Furthermore, our approach carefully preserves personalized enhancements on edge devices, maintaining model performance post-unlearning. Our results affirm the practicality and effectiveness of this verification framework, demonstrating verifiable unlearning with minimal degradation in personalization-induced performance improvements. Our methodology ensures verifiable, privacy-preserving, and effective machine unlearning across edge devices.
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