@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://preview.aclanthology.org/ingest-acl-workshops/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."
}