  Multi-source data fusion, in which multiple data sources are jointly analyzed
to obtain improved information, has considerable research attention. For the
datasets of multiple medical institutions, data confidentiality and
cross-institutional communication are critical. In such cases, data
collaboration (DC) analysis by sharing dimensionality-reduced intermediate
representations without iterative cross-institutional communications may be
appropriate. Identifiability of the shared data is essential when analyzing
data including personal information. In this study, the identifiability of the
DC analysis is investigated. The results reveals that the shared intermediate
representations are readily identifiable to the original data for supervised
learning. This study then proposes a non-readily identifiable DC analysis only
sharing non-readily identifiable data for multiple medical datasets including
personal information. The proposed method solves identifiability concerns based
on a random sample permutation, the concept of interpretable DC analysis, and
usage of functions that cannot be reconstructed. In numerical experiments on
medical datasets, the proposed method exhibits a non-readily identifiability
while maintaining a high recognition performance of the conventional DC
analysis. For a hospital dataset, the proposed method exhibits a nine
percentage point improvement regarding the recognition performance over the
local analysis that uses only local dataset.
