Differential privacy for medical deep learning: methods, tradeoffs, and deployment implications

npj Digital Medicine, vol. 9, pp. 93

Abstract

Differential privacy (DP) is a prominent technique for protecting sensitive patient data in medical deep learning (DL), yet deploying it without compromising clinical utility or equity remains challenging. This scoping review synthesizes applications of DP in medical DL across centralized and federated settings. A structured search identified 74 eligible studies published through March 2025. Across modalities and tasks, DP, especially via DP-SGD, can maintain clinically acceptable performance under moderate privacy budgets (ϵ ≈ 10), particularly in imaging. However, strict privacy (ϵ ≈ 1) often leads to substantial accuracy loss, with amplified degradation in smaller or heterogeneous datasets. Only a minority of studies evaluate fairness, and several report that DP can widen subgroup performance gaps. Beyond DP-SGD, alternative mechanisms, including generative modeling, local DP, and hybrid federated designs, are emerging, but reporting of privacy parameters remains inconsistent. We identify key gaps in fairness auditing and standardization, and outline priorities for equitable, clinically robust privacy-preserving DL.

Authors 6

1. Mohsen Vejdanihemmat

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

2. Mahshad Lotfinia Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

3. Mirabela Rusu

   Stanford Medicine · Stanford University

   Affiliation as printed

   Department of Radiology, Stanford University, Stanford, CA, USA

   Department of Urology, Stanford University, Stanford, CA, USA

4. Daniel Truhn Aachen Department of Diagnostic and Interventional Radiology

   Universitätsklinikum Aachen · RWTH Aachen University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany

5. Andreas Maier

   Friedrich-Alexander-Universität Erlangen-Nürnberg

   Affiliation as printed

   Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

6. Soroosh Tayebi Arasteh corresponding Aachen Department of Diagnostic and Interventional Radiology

   Friedrich-Alexander-Universität Erlangen-Nürnberg · Universitätsklinikum Aachen · Stanford Medicine · RWTH Aachen University · Stanford University

   Affiliation as printed

   Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany. soroosh.arasteh@rwth-aachen.de

   Department of Radiology, Stanford University, Stanford, CA, USA. soroosh.arasteh@rwth-aachen.de

   Department of Urology, Stanford University, Stanford, CA, USA. soroosh.arasteh@rwth-aachen.de

   Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany. soroosh.arasteh@rwth-aachen.de