Privacy-Preserving Detection of Rare Disease-Associated Cell Subsets via Secure Multi-Party Computation

2026-08-20

AI Summary

本文提出一种安全多方计算框架，用于在保护隐私的前提下训练和应用CellCnn模型以识别罕见疾病相关的细胞亚群。

Abstract

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 enables the training and inference of CellCnn entirely on secret-shared data. This ensures that neither the participants nor the computing servers ever observe raw patient data or intermediate values. Evaluated on benchmark single-cell datasets for cytomegalovirus infection (CMV) and acute myeloid leukaemia (AML), our implementation preserves accuracy close to its plaintext counterpart while outperforming the prior privacy-preserving baseline. In contrast to earlier privacy-preserving approaches that removed components such as ReLU activations and bias terms, our method retains these key parts of the CellCnn architecture and supports accurate analysis without exposing raw patient data.

Problem

• Privacy-Preserving
• Rare Disease-Associated Cell Subsets
• Secure Multi-Party Computation
• Single-Cell Data

Innovation

• secure multi-party computation
• CellCnn
• privacy-preserving