构建统一评估文本隐私化技术的开源平台，助力隐私保护研究
PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization
- 按多个评估维度模块化设计，覆盖隐私保护核心需求
- 支持实时评测与公开排名，推动技术迭代与竞争
- 开源免费可扩展，适合隐私计算与NLP研究者使用
自然语言处理为隐私保护带来新方法，尤其在文本到文本隐私化领域——将敏感文本转换为匿名化输出，理想情况下需屏蔽直接或间接身份信息。然而现有评估方法不统一，研究者采用多种技术与指标衡量隐私保护能力。为此，我们提出PrivBench：一个全面且模块化的基准测试平台，用于评估文本隐私化技术。PrivBench涵盖一系列预定义的理想特性，分模块组织；具备可扩展性，支持未来版本更新；以用户为中心，提供实时评估与公开排行榜。平台免费开放，网址为https://privbench.com/。
原文摘要 · Abstract (English)
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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