dpUGC: Learn Differentially Private Representation for User Generated Contents
Table 3: Regression performance on public embedding with and without privacy guarantee in comparison with not using public embedding. Evaluation score is RMSE. † marks good checkpoints to publish the DP-Embedding model, and LS stands for Learning_Step.
['LS', 'SVR Baseline-SVR', 'SVR DP-SVR', 'SVR NoneDP-SVR', 'LR Baseline-LR', 'LR DP-LR', 'LR NoneDP-LR', 'Privacy-Budget (0.125,  [ITALIC] δ)']
RMSE ( root mean square error) is used to evaluate the regression task. Lower RMSE proves better regression performance. This clearly shows that, with (ϵ,δ) privacy guarantee at some settings, such as (0.125,0.0184)-DP and (0.125,0.0197)-DP, we achieve the optimized trade-off of privacy-guarantee and data utilities.