With Privacy, Size Matters: On the Importance of Dataset Size in Differentially Private Text Rewriting

TL;DR

This paper investigates how dataset size impacts the effectiveness of differentially private text rewriting mechanisms in NLP, emphasizing the need for larger datasets for better privacy-utility trade-offs.

Contribution

It introduces the consideration of dataset size in evaluating DP NLP mechanisms and provides large-scale empirical analysis on datasets up to one million texts.

Findings

Larger datasets improve privacy-utility balance in DP text rewriting

Dataset size significantly influences the efficacy of DP NLP methods

Calls for more rigorous evaluation procedures in DP NLP

Abstract

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.