  Recently, data collaboration (DC) analysis has been developed for
privacy-preserving integrated analysis across multiple institutions. DC
analysis centralizes individually constructed dimensionality-reduced
intermediate representations and realizes integrated analysis via collaboration
representations without sharing the original data. To construct the
collaboration representations, each institution generates and shares a
shareable anchor dataset and centralizes its intermediate representation.
Although, random anchor dataset functions well for DC analysis in general,
using an anchor dataset whose distribution is close to that of the raw dataset
is expected to improve the recognition performance, particularly for the
interpretable DC analysis. Based on an extension of the synthetic minority
over-sampling technique (SMOTE), this study proposes an anchor data
construction technique to improve the recognition performance without
increasing the risk of data leakage. Numerical results demonstrate the
efficiency of the proposed SMOTE-based method over the existing anchor data
constructions for artificial and real-world datasets. Specifically, the
proposed method achieves 9 percentage point and 38 percentage point performance
improvements regarding accuracy and essential feature selection, respectively,
over existing methods for an income dataset. The proposed method provides
another use of SMOTE not for imbalanced data classifications but for a key
technology of privacy-preserving integrated analysis.
