Correlation-Guided Fast Machine Unlearning via Hessian Analysis

2026-09-11

AI Summary

本文提出一种基于Hessian分析的快速机器遗忘框架，通过识别训练集中的相关数据点并应用理论推导的参数更新规则，有效解决了现有方法计算成本高的问题。

Abstract

The increasing adoption of machine learning in network and distributed security systems has created an urgent need for mechanisms that can selectively and efficiently remove the influence of specific training data to eliminate compromised or adversarial data points from production models. Privacy regulations such as GDPR's \emph{right to be forgotten} also pose similar requirements. However, existing approximate unlearning techniques remain computationally prohibitive for deployment in real-world security systems, as they require repeated expensive Hessian-inverse-vector computations for each data point removal, creating a bottleneck when processing multiple related requests in scenarios such as intrusion detection systems, spam filters, and threat intelligence platforms. Thus, we introduce a computationally efficient unlearning framework that identifies correlated data points in the training set and applies a theoretically derived closed-form parameter update rule, achieving an $82\times$ wall-clock speedup over standard influence function unlearning while preserving model utility with a $10^{-2}$ improvement in accuracy over state-of-the-art baselines. Our method establishes theoretical guarantees and ensures numerical stability through Hessian damping. Our evaluation across seven diverse dataset architecture combinations, including large-scale CIFAR-100 with ResNet-50, demonstrates superior forgetting effectiveness, with membership inference attack success rates of 0.660 and tug-of-war scores of 0.950.

Problem

• machine unlearning
• Hessian analysis
• privacy regulations
• data removal
• security systems

Innovation

• Correlation-Guided
• Fast Machine Unlearning
• Hessian Analysis
• Theoretical Guarantees
• Numerical Stability