@inproceedings{meisenbacher-etal-2026-privbench,
title = "{P}riv{B}ench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization",
author = "Meisenbacher, Stephen and
Bodea, Andreea-Elena and
Akin, Ahmet Bilal and
Klymenko, Alexandra and
Diesner, Jana and
Matthes, Florian",
editor = "Agrawal, Sweta and
Abad, Alberto",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-demos.39/",
pages = "391--413",
ISBN = "979-8-89176-461-3",
abstract = "Natural Language Processing methods have enabled novel solutions and advances in the field of privacy, particularly in the sub-domain of *text-to-text privatization*, where the goal is to transform a sensitive input text into a privatized output by ideally masking (in)directly identifiable or otherwise private information. The evaluation of text-to-text privatization, however, is not straightforward, and the extant literature has utilized a myriad of techniques and metrics to quantify the privacy-preserving capabilities of privatization methods. Seeking to unify the evaluation of text-to-text privatization, we introduce PrivBench, a holistic and modular benchmarking platform for researchers and practitioners working on text privatization. PrivBench is holistic in that it evaluates privatization on a series of defined desiderata, which are structured into *modules*. PrivBench is not only modular but also extensible, allowing for future updates and benchmark versions. PrivBench is user-centered and promotes competition via real-time evaluation and a live public leaderboard. The platform is free to use and openly accessible at https://privbench.com/."
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%0 Conference Proceedings
%T PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization
%A Meisenbacher, Stephen
%A Bodea, Andreea-Elena
%A Akin, Ahmet Bilal
%A Klymenko, Alexandra
%A Diesner, Jana
%A Matthes, Florian
%Y Agrawal, Sweta
%Y Abad, Alberto
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-461-3
%F meisenbacher-etal-2026-privbench
%X Natural Language Processing methods have enabled novel solutions and advances in the field of privacy, particularly in the sub-domain of *text-to-text privatization*, where the goal is to transform a sensitive input text into a privatized output by ideally masking (in)directly identifiable or otherwise private information. The evaluation of text-to-text privatization, however, is not straightforward, and the extant literature has utilized a myriad of techniques and metrics to quantify the privacy-preserving capabilities of privatization methods. Seeking to unify the evaluation of text-to-text privatization, we introduce PrivBench, a holistic and modular benchmarking platform for researchers and practitioners working on text privatization. PrivBench is holistic in that it evaluates privatization on a series of defined desiderata, which are structured into *modules*. PrivBench is not only modular but also extensible, allowing for future updates and benchmark versions. PrivBench is user-centered and promotes competition via real-time evaluation and a live public leaderboard. The platform is free to use and openly accessible at https://privbench.com/.
%U https://aclanthology.org/2026.emnlp-demos.39/
%P 391-413
Markdown (Informal)
[PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://aclanthology.org/2026.emnlp-demos.39/) (Meisenbacher et al., EMNLP 2026)
ACL