DeepUL: Deep Unlearning via Model Sparsity

Deep neural networks (DNNs) have achieved remarkable success across various domains, but their reliance on large datasets often containing sensitive information raises significant privacy concerns. In response to stringent privacy regulations such as GDPR and CCPA, machine unlearning (MUL) has emerged as a critical technique to remove the influence of specific data samples from trained models. However, existing unlearning methods face two major challenges: privacy deficiencies in the remaining datasets and significant instability of the model after unlearning. To address these issues, we propose DeepUL, a deep unlearning algorithm that leverages model sparsity to enhance both privacy and stability. DeepUL follows a two-step process: first pruning and then unlearning. The pruning step not only sparsifies the model but also decouples the model parameters from the original training data, thereby improving privacy. The unlearning step employs gradient projection to eliminate dependencies on the data to be deleted while maintaining the model stability. Additionally, we introduce a hierarchical weight pruning strategy to achieve differential pruning across network layers, preventing layer collapse and ensuring robustness. Extensive experiments on benchmark datasets demonstrate that DeepUL outperforms existing methods in utility, unlearning efficacy, privacy guarantee, and stability, making it a practical and effective solution for machine unlearning.