With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting
This work highlights a critical but often overlooked factor in DP NLP evaluation, calling for more rigorous procedures to improve practical scalability.
The study investigated how dataset size affects the privacy-utility trade-off in differentially private text rewriting, finding that larger datasets (up to one million texts) significantly impact evaluation outcomes.
Recent work in Differential Privacy with Natural Language Processing (DP NLP) has proposed numerous promising techniques in the form of text rewriting mechanisms. In the evaluation of these mechanisms, an often-ignored aspect is that of dataset size, or rather, the effect of dataset size on a mechanism's efficacy for utility and privacy preservation. In this work, we are the first to introduce this factor in the evaluation of DP text privatization, where we design utility and privacy tests on large-scale datasets with dynamic split sizes. We run these tests on datasets of varying size with up to one million texts, and we focus on quantifying the effect of increasing dataset size on the privacy-utility trade-off. Our findings reveal that dataset size plays an integral part in evaluating DP text rewriting mechanisms; additionally, these findings call for more rigorous evaluation procedures in DP NLP, as well as shed light on the future of DP NLP in practice and at scale.