Non-readily identifiable data collaboration analysis for multiple datasets including personal information. (arXiv:2208.14611v1 [cs.LG])

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 …

analysis arxiv collaboration data data collaboration datasets information