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A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation

Summary

This study systematically evaluates text decomposition and privacy budget distribution techniques in differentially private text obfuscation, finding that design choices significantly impact privacy-utility trade-offs and providing evidence for optimizing DP obfuscation procedures.

SourcearXiv Computational LinguisticsAuthor: Stephen Meisenbacher, Angelo Kleinert, Florian Matthes
A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation
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[2605.01065] A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation

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Computer Science > Computation and Language

arXiv:2605.01065 (cs)

[Submitted on 1 May 2026]

Title:A Systematic Exploration of Text Decomposition and Budget Distribution in Differentially Private Text Obfuscation

Authors:Stephen Meisenbacher, Angelo Kleinert, Florian Matthes

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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 $\varepsilon$ 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.

Comments: 22 pages, 5 figures, 12 tables. Accepted to PrivateNLP 2026

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2605.01065 [cs.CL]

(or arXiv:2605.01065v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.01065

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arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Stephen Meisenbacher [view email] [v1] Fri, 1 May 2026 20:00:14 UTC (7,104 KB)

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Key points

  • Differentially private text obfuscation aims to perturb input texts with quantifiable privacy guarantees.
  • The study compares multiple methods for chunking texts and allocating privacy budget (ε).
  • Experiments show that different method combinations yield significantly different results even with comparable budgets.
  • Optimizing DP obfuscation procedures can improve empirical privacy-utility trade-offs.

Highlights and analysis are generated automatically and may contain errors. Check the original source.