Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation
The detection of rare disease-associated cell subsets from high-dimensional single-cell measurements is critical for understanding diseases such as leukaemia and viral infections. CellCnn, a convolutional neural network (CNN) designed for this task, has demonstrated the ability to identify phenotype-associated cell populations at frequencies as low as 0.01\%. Training such models reliably requires patient cohorts that are larger and more diverse than any single institution can typically assemble, and the underlying single-cell data is too sensitive to share across institutional boundaries under existing privacy regulations. We propose a secure multi-party computation (MPC) framework that ...
| Authors | Ş. Selcan Magara, Esther Havemann, Debora Jutz et al. |
|---|---|
| Primary category | cs.CR |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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