Leveraging Hierarchical Representationsfor Preserving Privacy and Utility in Text
TABLE VI: Accuracy scores on classification tasks. * indicates results better than 1 baseline, ** better than 2 baselines
['[BOLD] dataset', '[ITALIC] random', 'hyp-100 [ITALIC] d  [ITALIC] ε [BOLD] =0.125', 'hyp-100 [ITALIC] d  [ITALIC] ε [BOLD] =1', 'hyp-100 [ITALIC] d  [ITALIC] ε [BOLD] =8', '[ITALIC] original InferSent', '[ITALIC] original SkipThought', '[ITALIC] original fastText-BoV']
The results are presented in Tab. The evaluation metric for all tasks was accuracy on the test set. Across all the experiments, our algorithm yielded better results that just replacing with random words. In addition, and as expected, at lower values of ε=0.125, we record lower values of utility across all tasks. Conversely at the higher value of ε=8, the accuracy scores get closer to the baselines. All the results illustrate the tradeoff between privacy and utility. It also shows that we can achieve tunable privacy guarantees with minimal impact on the utility of downstream ML models.