Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data. (arXiv:2308.04755v1 [cs.LG])

Consider a setting where multiple parties holding sensitive data aim to
collaboratively learn population level statistics, but pooling the sensitive
data sets is not possible. We propose a framework in which each party shares a
differentially private synthetic twin of their data. We study the feasibility
of combining such synthetic twin data sets for collaborative learning on
real-world health data from the UK Biobank. We discover that parties engaging
in the collaborative learning via shared synthetic data obtain more accurate …

aim arxiv collaborative data data sets distributed distributed data framework learn multiple pooling population shares statistics study synthetic