In the context of the rapid development of large language models (LLMs), contrastive learning has become widely adopted due to its ability to bypass costly data annotation by leveraging vast amounts of network data for model training. However, this widespread use raises significant concerns regarding data privacy protection. Unlearnable Examples (UEs), a technique that disrupts model learning by perturbing data, effectively prevents unauthorized models from misusing sensitive data. However, existing methods for generating UEs face two primary challenges: first, perturbations may be reversed using techniques such as reverse purification or denoising, including diffusion models that remove protective perturbations in image UEs; second, once data is published, ensuring data traceability and managing access control becomes difficult. To address these issues, this paper proposes a Blockchain-Integrated Unlearnable Example Generation and Management Framework (B-UEGMF) for generating and managing UEs. By leveraging the decentralized and immutable properties of blockchain, we store example hash values on the blockchain and dynamically manage data access rights through smart contracts. Additionally, UEs are generated using a multi-objective perturbation technique, Dynamic Error-Minimizing Noise (DEM), which enhances robustness against reversal methods. We also provide a quantitative evaluation of the privacy protection capabilities of the generated examples. Experimental results demonstrate that the proposed framework significantly improved the defense of UEs against reverse attacks while ensuring efficient data privacy management.
Li et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: