Privacy-preserving Data Sharing on Vertically Partitioned Data. (arXiv:2010.09293v2 [cs.LG] UPDATED)

In this work, we introduce a differentially private method for generating
synthetic data from vertically partitioned data, \emph{i.e.}, where data of the
same individuals is distributed across multiple data holders or parties. We
present a differentially privacy stochastic gradient descent (DP-SGD) algorithm
to train a mixture model over such partitioned data using variational
inference. We modify a secure multiparty computation (MPC) framework to combine
MPC with differential privacy (DP), in order to use differentially private MPC
effectively to learn a …

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