Leveraging Hierarchical Representationsfor Preserving Privacy and Utility in Text
TABLE IV: Privacy comparisons (lower Nw is better)
['[ITALIC] ε', 'worst-case  [ITALIC] Nw', 'expected value  [ITALIC] Nw hyp-100', 'expected value  [ITALIC] Nw euc-100', 'expected value  [ITALIC] Nw euc-200', 'expected value  [ITALIC] Nw euc-300']
The results for the comparative privacy analysis are presented in Tab. The results clearly demonstrate that for identical worst case guarantees, the expected case for the Hyperbolic model is significantly better than the Euclidean across all Euclidean dimensions. Combining this with the superior ability of the Hyperbolic model to encode both similarity and hierarchy even at lower dimensions provides a strong argument for adopting it as a dχ-privacy preserving mechanism for the motivating examples described in Sec.