DP-SSLoRA: A privacy-preserving medical classification model combining differential privacy with self-supervised low-rank adaptation

• Published in: Computers in biology and medicine 2024-09, Vol.179, p.108792, Article 108792
• Main Authors: Yan, Chaokun, Yan, Haicao, Liang, Wenjuan, Yin, Menghan, Luo, Huimin, Luo, Junwei
• Format: Article
• Language: English
• Subjects: Adaptation; Algorithms; Background noise; Chest; Classification; COVID-19; Datasets; Deep Learning; Differential privacy; DP-SGD; Humans; Image enhancement; Low-rank adaption; Medical image classification; Medical imaging; Privacy; Random noise; SARS-CoV-2; Self-supervised learning; Source code
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108792

Concerns about patient privacy issues have limited the application of medical deep learning models in certain real-world scenarios. Differential privacy (DP) can alleviate this problem by injecting random noise into the model. However, naively applying DP to medical models will not achieve a satisfactory balance between privacy and utility due to the high dimensionality of medical models and the limited labeled samples. This work proposed the DP-SSLoRA model, a privacy-preserving classification model for medical images combining differential privacy with self-supervised low-rank adaptation. In this work, a self-supervised pre-training method is used to obtain enhanced representations from unlabeled publicly available medical data. Then, a low-rank decomposition method is employed to mitigate the impact of differentially private noise and combined with pre-trained features to conduct the classification task on private datasets. In the classification experiments using three real chest-X ray datasets, DP-SSLoRA achieves good performance with strong privacy guarantees. Under the premise of ɛ=2, with the AUC of 0.942 in RSNA, the AUC of 0.9658 in Covid-QU-mini, and the AUC of 0.9886 in Chest X-ray 15k. Extensive experiments on real chest X-ray datasets show that DP-SSLoRA can achieve satisfactory performance with stronger privacy guarantees. This study provides guidance for studying privacy-preserving in the medical field. Source code is publicly available online. https://github.com/oneheartforone/DP-SSLoRA. •A differentially private medical image classification model is proposed.•Low-rank adaption and self supervised preprocessing are combined to reduce DP noise.•The private model achieves a better trade-off between privacy and utility.