Privacy-Preserving Model and Preprocessing Verification for Machine Learning
This addresses privacy concerns for organizations handling sensitive data, though it appears incremental as it combines existing techniques.
The paper tackles the problem of verifying machine learning models trained on sensitive data while preserving privacy, by integrating Local Differential Privacy with model explanations like LIME and SHAP. Results show the framework effectively detects preprocessing errors across datasets like Diabetes, Adult, and Student Record, with varying accuracy depending on noise levels.
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.