@inproceedings{meisenbacher-etal-2026-systematic,
title = "A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation",
author = "Meisenbacher, Stephen and
Kleinert, Angelo and
Matthes, Florian",
editor = "Habernal, Ivan and
Ghanavati, Sepideh and
Haghighi, Sara and
Ramesh, Krithika and
Igamberdiev, Timour and
Wilson, Shomir",
booktitle = "Proceedings of the Seventh Workshop on Privacy in Natural Language Processing",
month = jul,
year = "2026",
address = "San Diego, California",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.privatenlp-main.9/",
doi = "10.18653/v1/2026.privatenlp-main.9",
pages = "118--139",
ISBN = "979-8-89176-397-5",
abstract = "The goal of *differentially private text obfuscation* is to obfuscate, or ``perturb'', input texts with Differential Privacy (DP) guarantees, such that the private output texts are quantifiably indistinguishable from the originals. While perturbation at the word level is intuitive, meaningful text privatization happens on complete documents. Recent research has laid the groundwork for reasoning about *privacy budget distribution*, namely, how an overall $\varepsilon$ budget can be sensibly distributed among the component pieces of a text. We perform a systematic evaluation of multiple text decomposition and budget distribution techniques in the context of DP text obfuscation, testing how different methods for chunking texts can be combined with techniques for allocating $\varepsilon$ to these chunks. Our experiments reveal that such design choices are very important, as even with comparable privacy budgets, significantly different results can occur based on which methods are chosen. In this, we provide credible evidence of the feasibility of maximizing empirical trade-offs by optimizing DP obfuscation procedures."
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<abstract>The goal of *differentially private text obfuscation* is to obfuscate, or “perturb”, input texts with Differential Privacy (DP) guarantees, such that the private output texts are quantifiably indistinguishable from the originals. While perturbation at the word level is intuitive, meaningful text privatization happens on complete documents. Recent research has laid the groundwork for reasoning about *privacy budget distribution*, namely, how an overall ǎrepsilon budget can be sensibly distributed among the component pieces of a text. We perform a systematic evaluation of multiple text decomposition and budget distribution techniques in the context of DP text obfuscation, testing how different methods for chunking texts can be combined with techniques for allocating ǎrepsilon to these chunks. Our experiments reveal that such design choices are very important, as even with comparable privacy budgets, significantly different results can occur based on which methods are chosen. In this, we provide credible evidence of the feasibility of maximizing empirical trade-offs by optimizing DP obfuscation procedures.</abstract>
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%0 Conference Proceedings
%T A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
%A Meisenbacher, Stephen
%A Kleinert, Angelo
%A Matthes, Florian
%Y Habernal, Ivan
%Y Ghanavati, Sepideh
%Y Haghighi, Sara
%Y Ramesh, Krithika
%Y Igamberdiev, Timour
%Y Wilson, Shomir
%S Proceedings of the Seventh Workshop on Privacy in Natural Language Processing
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California
%@ 979-8-89176-397-5
%F meisenbacher-etal-2026-systematic
%X The goal of *differentially private text obfuscation* is to obfuscate, or “perturb”, input texts with Differential Privacy (DP) guarantees, such that the private output texts are quantifiably indistinguishable from the originals. While perturbation at the word level is intuitive, meaningful text privatization happens on complete documents. Recent research has laid the groundwork for reasoning about *privacy budget distribution*, namely, how an overall ǎrepsilon budget can be sensibly distributed among the component pieces of a text. We perform a systematic evaluation of multiple text decomposition and budget distribution techniques in the context of DP text obfuscation, testing how different methods for chunking texts can be combined with techniques for allocating ǎrepsilon to these chunks. Our experiments reveal that such design choices are very important, as even with comparable privacy budgets, significantly different results can occur based on which methods are chosen. In this, we provide credible evidence of the feasibility of maximizing empirical trade-offs by optimizing DP obfuscation procedures.
%R 10.18653/v1/2026.privatenlp-main.9
%U https://aclanthology.org/2026.privatenlp-main.9/
%U https://doi.org/10.18653/v1/2026.privatenlp-main.9
%P 118-139
Markdown (Informal)
[A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation](https://aclanthology.org/2026.privatenlp-main.9/) (Meisenbacher et al., PrivateNLP 2026)
ACL