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.

翻译：在若干应用中,最近通过共享维度减少数据表示方式进行的合作数据分析被提议为非模式的共享型联合学习。本文分析了这一新框架的准确性和隐私评估。在准确性分析中,我们为合作数据分析与集中分析的等同性提供了充分的条件,降低了维度。在隐私分析中,我们证明合作用户的私人数据集受到保护,有双重隐私层,防止内部和外部攻击。