Secure Multiparty Computation for Synthetic Data Generation from Distributed Data. (arXiv:2210.07332v1 [cs.CR])

Legal and ethical restrictions on accessing relevant data inhibit data
science research in critical domains such as health, finance, and education.
Synthetic data generation algorithms with privacy guarantees are emerging as a
paradigm to break this data logjam. Existing approaches, however, assume that
the data holders supply their raw data to a trusted curator, who uses it as
fuel for synthetic data generation. This severely limits the applicability, as
much of the valuable data in the world is locked up …

computation data distributed secure multiparty computation synthetic data