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arXiv — NLP / Computation & Language · · 3 min read

PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization

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Computer Science > Computation and Language

arXiv:2608.29624 (cs)
[Submitted on 30 Aug 2026]

Title:PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization

View a PDF of the paper titled PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization, by Stephen Meisenbacher and 5 other authors
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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 this https URL.
Comments: 23 pages, 5 figures, 3 tables, accepted to EMNLP 2026 System Demonstrations
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2608.29624 [cs.CL]
  (or arXiv:2608.29624v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2608.29624
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Stephen Meisenbacher [view email]
[v1] Sun, 30 Aug 2026 07:33:08 UTC (3,100 KB)
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