Verifiable Unlearning on Edge

• URL: http://arxiv.org/abs/2506.20037v1
• Date: Tue, 24 Jun 2025 22:24:47 GMT
• Title: Verifiable Unlearning on Edge
• Authors: Mohammad M Maheri, Alex Davidson, Hamed Haddadi,
• Abstract summary: We introduce a verification framework leveraging zero-knowledge proofs, specifically zk-SNARKs, to confirm data unlearning on personalized edge-device models without compromising privacy.<n>We have developed algorithms explicitly designed to facilitate unlearning operations compatible with efficient zk-SNARK proof generation.<n>Our results affirm the practicality and effectiveness of this verification framework, demonstrating verifiable unlearning with minimal degradation in personalization-induced performance improvements.
• Score: 6.761197166421685
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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