FusionDP: Foundation Model-Assisted Differentially Private Learning for Partially Sensitive Features

• URL: http://arxiv.org/abs/2511.03806v1
• Date: Wed, 05 Nov 2025 19:13:10 GMT
• Title: FusionDP: Foundation Model-Assisted Differentially Private Learning for Partially Sensitive Features
• Authors: Linghui Zeng, Ruixuan Liu, Atiquer Rahman Sarkar, Xiaoqian Jiang, Joyce C. Ho, Li Xiong,
• Abstract summary: In practical scenarios, privacy protection may be required for only a subset of features.<n>Traditional DP-SGD enforces privacy protection on all features in one sample, leading to excessive noise injection and significant utility degradation.<n>We propose FusionDP, a two-step framework that enhances model utility under feature-level differential privacy.
• Score: 17.945111987608865
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Ensuring the privacy of sensitive training data is crucial in privacy-preserving machine learning. However, in practical scenarios, privacy protection may be required for only a subset of features. For instance, in ICU data, demographic attributes like age and gender pose higher privacy risks due to their re-identification potential, whereas raw lab results are generally less sensitive. Traditional DP-SGD enforces privacy protection on all features in one sample, leading to excessive noise injection and significant utility degradation. We propose FusionDP, a two-step framework that enhances model utility under feature-level differential privacy. First, FusionDP leverages large foundation models to impute sensitive features given non-sensitive features, treating them as external priors that provide high-quality estimates of sensitive attributes without accessing the true values during model training. Second, we introduce a modified DP-SGD algorithm that trains models on both original and imputed features while formally preserving the privacy of the original sensitive features. We evaluate FusionDP on two modalities: a sepsis prediction task on tabular data from PhysioNet and a clinical note classification task from MIMIC-III. By comparing against privacy-preserving baselines, our results show that FusionDP significantly improves model performance while maintaining rigorous feature-level privacy, demonstrating the potential of foundation model-driven imputation to enhance the privacy-utility trade-off for various modalities.

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