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Distributed data analysis without revealing the individual data has recentlyattracted significant attention in several applications. A collaborative dataanalysis through sharing dimensionality reduced representations of data hasbeen proposed as a non-model sharing-type federated learning. This paperanalyzes the accuracy and privacy evaluations of this novel framework. In theaccuracy analysis, we provided sufficient conditions for the equivalence of thecollaborative data analysis and the centralized analysis with dimensionalityreduction. In the privacy analysis, we proved that collaborative users' privatedatasets are protected with a double privacy layer against insider and externalattacking scenarios.

Accuracy and Privacy Evaluations of Collaborative Data Analysis