title: Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data
Creators: name: Ziegler, Joceline
name: Pfitzner, Bjarne
name: Schulz, Heinrich
name: Saalbach, Axel
name: Arnrich, Bert
Publisher: name: Universitätsverlag Potsdam
Year: 2023
Subjects: subject: 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten

description: settingsOrder Article Reprints Open AccessArticle Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data by Joceline Ziegler 1,2,*ORCID,Bjarne Pfitzner 1,2ORCID,Heinrich Schulz 3ORCID,Axel Saalbach 3ORCID andBert Arnrich 1,2ORCID 1 Digital Engineering Faculty, University of Potsdam, 14482 Potsdam, Germany 2 Hasso Plattner Institute for Digital Engineering gGmbH, 14482 Potsdam, Germany 3 Philips Research, 22335 Hamburg, Germany * Author to whom correspondence should be addressed. Sensors 2022, 22(14), 5195; https://doi.org/10.3390/s22145195 Received: 6 May 2022 / Revised: 30 June 2022 / Accepted: 5 July 2022 / Published: 11 July 2022 (This article belongs to the Special Issue Next Generation of Secure and Resilient Healthcare Data Processing) Download Browse Figures Versions Notes Abstract Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10} . The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.94 for ε=6 . Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training.
description: Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät; 14