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Verticox+:垂直分布的Cox比例危险模型,提供了更好的隐私保障
Florian van Daalen1,2, Djura Smits3, Lianne Ippel4
1Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, The Netherlands.
概括
联合学习使得分散数据的隐私保护分析成为可能. Verticox+ 增强了 Verticox 模型的生存结果,在不共享敏感数据的情况下保持性能.
科学领域:
- 计算生物学是一种计算生物学.
- 机器学习是机器学习.
- 保护隐私的技术 保护隐私的技术
背景情况:
- 联合学习 (FL) 能够实现去中心化机器学习,而无需共享数据,这对隐私至关重要.
- 考克斯的比例危险模型对于生存分析至关重要.
- 像Verticox这样的现有联合模型需要对生存结果的本地知识,限制了适用性.
研究的目的:
- 扩展Verticox模型用于联合生存分析.
- 为了使Verticox在当地的生存结果无法获得时能够发挥作用.
- 在整个联合分析过程中确保隐私保护.
主要方法:
- 开发了Verticox+,这是Verticox联合学习模型的扩展.
- 整合了一个保护隐私的双方标量产品协议.
- 整合了协议,以处理缺乏本地生存结果数据的场景.
主要成果:
- Verticox+实现了与原始Verticox模型相当的性能.
- 在局部未知生存结果的场景中,已证明成功应用.
- 分析了对计算复杂性和通信成本的影响.
结论:
- Verticox+成功地扩展了联合的Cox比例危险建模.
- 保护隐私的协议允许更广泛地应用联合生存分析.
- 该方法保持了分析性能,同时增强了数据隐私.
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