New solutions based on the generalized eigenvalue problem for the data collaboration analysis
Yuta Kawakami, Yuichi Takano, Akira Imakura · Information Sciences · 2025
This paper is concerned with the data collaboration (DC) analysis, a privacy-preserving method for analyzing decentralized datasets held by multiple parties. In this method, privacy-preserving intermediate representations of original datasets are collected from multiple parties and then converted into collaboration representations for collaborative data analysis. However, conventional methods for creating collaboration representations suffer from several challenges; namely, the optimization problem being considered is not well defined, and the process of solving it is very difficult to understand. We thus propose a new solution for creating high-quality collaboration representations for the DC analysis. Specifically, we formulate a revised optimization problem for creating collaboration representations and then transform this optimization problem into a generalized eigenvalue problem. We also propose a reduction of the generalized eigenvalue problem to a singular value decomposition through the QR decomposition. Computational experiments using publicly available datasets demonstrate that our method can outperform the conventional methods for the DC analysis in terms of both prediction accuracy and computational efficiency. • Privacy-preserving data collaboration for analyzing decentralized datasets. • Generalized eigenvalue problem for high-quality collaboration representations. • Reduction of the generalized eigenvalue problem to a singular value decomposition. • Superiority of our method evaluated through computational experiments.