In recent years, the accumulation of data across various institutions has
garnered attention for the technology of confidential data analysis, which
improves analytical accuracy by sharing data between multiple institutions
while protecting sensitive information. Among these methods, Data Collaboration
Analysis (DCA) is noted for its efficiency in terms of computational cost and
communication load, facilitating data sharing and analysis across different
institutions while safeguarding confidential information. However, existing
optimization problems for determining the necessary collaborative functions
have faced challenges, such as the optimal solution for the collaborative
representation often being a zero matrix and the difficulty in understanding
the process of deriving solutions. This research addresses these issues by
formulating the optimization problem through the segmentation of matrices into
column vectors and proposing a solution method based on the generalized
eigenvalue problem. Additionally, we demonstrate methods for constructing
collaborative functions more effectively through weighting and the selection of
efficient algorithms suited to specific situations. Experiments using
real-world datasets have shown that our proposed formulation and solution for
the collaborative function optimization problem achieve superior predictive
accuracy compared to existing methods.

近年来，随着各个机构间数据的积累，保密数据分析技术备受关注，它通过在多个机构间共享数据来提高分析准确性，同时保护敏感信息。其中，数据协同分析（DCA）作为一种高效的计算成本和通信负载的方法，在保护机密信息的同时促进了不同机构间数据共享和分析。然而，现有的优化问题在确定必要的协同函数方面面临挑战，例如协同表示的最优解通常是零矩阵，以及理解解决过程的难度。本研究通过将矩阵分割为列向量，并提出一种基于广义特征值问题的解决方法，解决了这些问题。此外，我们还演示了通过加权和选择适用于特定情况的高效算法来更有效地构建协同函数的方法。使用真实数据集进行的实验证明，我们拟议的协同函数优化问题的公式和解决方法相比现有方法具有更好的预测准确性。