Before revision:  We address these challenges by adapting differentially-private gradient-based training to GCNs . We investigate the impact of various privacy budgets, dataset sizes, and two optimizers in an experimental setup over five NLP datasets in two languages  .
After revision:  We address these challenges by adapting differentially-private gradient-based training to GCNs and conduct experiments using two optimizers on five NLP datasets in two languages .