  Distributed data analysis without revealing the individual data has recently
attracted significant attention in several applications. A collaborative data
analysis through sharing dimensionality reduced representations of data has
been proposed as a non-model sharing-type federated learning. This paper
analyzes the accuracy and privacy evaluations of this novel framework. In the
accuracy analysis, we provided sufficient conditions for the equivalence of the
collaborative data analysis and the centralized analysis with dimensionality
reduction. In the privacy analysis, we proved that collaborative users' private
datasets are protected with a double privacy layer against insider and external
attacking scenarios.
