Collaborative Learning From Distributed Data With Differentially Private
  Synthetic Twin Data
Lukas Prediger Joonas Jälkö Antti Honkela Samuel Kaski
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
estimates of target statistics compared to using only their local data. This
finding extends to the difficult case of small heterogeneous data sets.
Furthermore, the more parties participate, the larger and more consistent the
improvements become. Finally, we find that data sharing can especially help
parties whose data contain underrepresented groups to perform better-adjusted
analysis for said groups. Based on our results we conclude that sharing of
synthetic twins is a viable method for enabling learning from sensitive data
without violating privacy constraints even if individual data sets are small or
do not represent the overall population well. The setting of distributed
sensitive data is often a bottleneck in biomedical research, which our study
shows can be alleviated with privacy-preserving collaborative learning methods.