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Collaborative Learning From Distributed Data With Differentially Private Synthetic Twin Data. (arXiv:2308.04755v1 [cs.LG])
cs.CR updates on arXiv.org arxiv.org
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 data data sets distributed framework learn party private sensitive data statistics study synthetic