用隐私保护方法验证机器学习数据预处理是否正确。
Privacy-Preserving Model and Preprocessing Verification for Machine Learning
- 结合局部差分隐私与模型解释工具，保护隐私的同时验证模型训练过程。
- 在糖尿病等三个真实数据集上，二分类任务验证准确率较高，多分类也表现稳定。
- 适合关注数据隐私的AI安全研究人员和医疗、金融领域从业者。
本文提出一种面向敏感数据训练机器学习模型的隐私保护验证框架。通过将局部差分隐私（LDP）与LIME、SHAP等模型解释方法结合，实现不泄露个体隐私的前提下对模型训练过程进行有效验证。针对两类核心任务：二分类任务用于判断目标模型是否正确执行了预处理步骤；多分类任务用于识别具体预处理错误类型。在糖尿病（Diabetes）、成人收入（Adult）、学生记录（Student Record）三个真实数据集上的评估表明，基于机器学习的方法在二分类任务中表现优异，而基于阈值的方法在多分类任务中表现相当。结果表明，尽管不同数据集和噪声水平下验证准确率有所差异，该框架仍能有效检测预处理错误，同时提供强隐私保障，并具备实际应用可行性。
原文摘要 · Abstract (English)
This paper presents a framework for privacy-preserving verification of machine learning models, focusing on models trained on sensitive data. Integrating Local Differential Privacy (LDP) with model explanations from LIME and SHAP, our framework enables robust verification without compromising individual privacy. It addresses two key tasks: binary classification, to verify if a target model was trained correctly by applying the appropriate preprocessing steps, and multi-class classification, to identify specific preprocessing errors. Evaluations on three real-world datasets-Diabetes, Adult, and Student Record-demonstrate that while the ML-based approach is particularly effective in binary tasks, the threshold-based method performs comparably in multi-class tasks. Results indicate that although verification accuracy varies across datasets and noise levels, the framework provides effective detection of preprocessing errors, strong privacy guarantees, and practical applicability for safeguarding sensitive data.
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