  Privately generating synthetic data from a table is an important brick of a
privacy-first world. We propose and investigate a simple approach of treating
each row in a table as a sentence and training a language model with
differential privacy. We show this approach obtains competitive results in
modelling tabular data across multiple datasets, even at small scales that
favor alternative methods based on marginal distributions.

Machine Learning;Computation and Language;Cryptography and Security