Distributed learning optimisation of Cox models can leak patient data: Risks and solutions Article Swipe
Carsten Brink
,
Christian Rønn Hansen
,
Matt Field
,
Gareth J. Price
,
David Thwaites
,
Nis Sarup
,
Uffe Bernchou
,
Lois Holloway
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.05856
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2204.05856
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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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2204.05856
- https://arxiv.org/pdf/2204.05856
- OA Status
- green
- Cited By
- 5
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4224087343
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4224087343Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2204.05856Digital Object Identifier
- Title
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Distributed learning optimisation of Cox models can leak patient data: Risks and solutionsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-04-12Full publication date if available
- Authors
-
Carsten Brink, Christian Rønn Hansen, Matt Field, Gareth J. Price, David Thwaites, Nis Sarup, Uffe Bernchou, Lois HollowayList of authors in order
- Landing page
-
https://arxiv.org/abs/2204.05856Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2204.05856Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2204.05856Direct OA link when available
- Concepts
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Computer science, Missing data, MATLAB, Federated learning, Data modeling, Leak, Data mining, Code (set theory), Machine learning, Artificial intelligence, Database, Engineering, Operating system, Environmental engineering, Set (abstract data type), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 2, 2022: 1Per-year citation counts (last 5 years)
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-
10Other works algorithmically related by OpenAlex
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