Distributed learning optimisation of Cox models can leak patient data: Risks and solutions

• URL: http://arxiv.org/abs/2204.05856v1
• Date: Tue, 12 Apr 2022 14:56:20 GMT
• Title: Distributed learning optimisation of Cox models can leak patient data: Risks and solutions
• Authors: Carsten Brink (1,2) and Christian R{\o}nn Hansen (1,2) and Matthew Field (3,4) and Gareth Price (5) and David Thwaites (6) and Nis Sarup (1) and Uffe Bernchou (1,2) and Lois Holloway (3,4,6,7) ((1) Laboratory of Radiation Physics, Department of Oncology, Odense University Hospital, Odense, Denmark, (2) Department of Clinical Research, University of Southern Denmark, Odense, Denmark, (3) South Western Sydney Clinical School, Faculty of Medicine, UNSW, Sydney, New South Wales, Australia, (4) Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia, (5) The University of Manchester, Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK, (6) Institute of Medical Physics, School of Physics, University of Sydney, Sydney, New South Wales, Australia, (7) Liverpool and Macarthur Cancer Therapy Centres, Liverpool, New South Wales, Australia)
• Abstract summary: This paper demonstrates that the optimisation of a Cox survival model can lead to patient data leakage. We suggest a way to optimise and validate a Cox model that avoids these problems in a secure way.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Medical data are often highly sensitive, and frequently there are missing data. Due to the data's sensitive nature, there is an interest in creating modelling methods where the data are kept in each local centre to preserve their privacy, but yet the model can be trained on and learn from data across multiple centres. Such an approach might be distributed machine learning (federated learning, collaborative learning) in which a model is iteratively calculated based on aggregated local model information from each centre. However, even though no specific data are leaving the centre, there is a potential risk that the exchanged information is sufficient to reconstruct all or part of the patient data, which would hamper the safety-protecting rationale idea of distributed learning. This paper demonstrates that the optimisation of a Cox survival model can lead to patient data leakage. Following this, we suggest a way to optimise and validate a Cox model that avoids these problems in a secure way. The feasibility of the suggested method is demonstrated in a provided Matlab code that also includes methods for handling missing data.

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