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

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…

• EC
  European Commission

  Award: 101057091
• DF
  Deutsche Forschungsgemeinschaft

  Awards: LI3893/6-1, 2136/3-1, NE 2136/3-1, TR 1700/7-1
• HE
  HORIZON EUROPE Framework Programme

  Award: 101057091