Privasis：从零开始合成最大的“公共”私有数据集
Privasis: Synthesizing the Largest "Public" Private Dataset from Scratch
AI总结：
Privasis是首个从零开始构建的百万级合成数据集，用于提升隐私敏感领域研究的规模和效率，其模型在隐私净化任务中表现优异。
摘要：
涉及隐私敏感数据的研究一直受到数据稀缺的限制，这与其它领域受益于数据扩展形成鲜明对比。随着现代AI代理（如OpenClaw和Gemini Agent）被授予持续访问高度敏感的个人信息的权限，这一挑战变得越来越紧迫。为解决这一长期瓶颈和日益增长的风险，我们提出了Privasis（即隐私绿洲），这是首个百万级的完全合成数据集，完全从零开始构建——一个包含丰富多样隐私信息的广阔文本仓库，旨在扩大和加速必须处理敏感社会数据的领域的研究。与现有数据集相比，Privasis包含140万条记录，提供了数量级更大的规模和质量，并在各种文档类型中提供了远大于现有数据集的多样性，包括医疗记录、法律文件、财务记录、日历和文本信息，总计5510万条标注属性，如种族、出生日期、工作场所等。我们利用Privasis构建了一个平行语料库，用于文本净化，我们的管道分解文本并应用针对性的净化。我们的紧凑型净化模型（<=4B）在该数据集上训练后，优于最先进的大型语言模型，如GPT-5和Qwen-3 235B。我们计划发布数据、模型和代码，以加速未来隐私敏感领域的研究和代理。
英文摘要：
Research involving privacy-sensitive data has always been constrained by data scarcity, standing in sharp contrast to other areas that have benefited from data scaling. This challenge is becoming increasingly urgent as modern AI agents--such as OpenClaw and Gemini Agent--are granted persistent access to highly sensitive personal information. To tackle this longstanding bottleneck and the rising risks, we present Privasis (i.e., privacy oasis), the first million-scale fully synthetic dataset entirely built from scratch--an expansive reservoir of texts with rich and diverse private information--designed to broaden and accelerate research in areas where processing sensitive social data is inevitable. Compared to existing datasets, Privasis, comprising 1.4 million records, offers orders-of-magnitude larger scale with quality, and far greater diversity across various document types, including medical history, legal documents, financial records, calendars, and text messages with a total of 55.1 million annotated attributes such as ethnicity, date of birth, workplace, etc. We leverage Privasis to construct a parallel corpus for text sanitization with our pipeline that decomposes texts and applies targeted sanitization. Our compact sanitization models (<=4B) trained on this dataset outperform state-of-the-art large language models, such as GPT-5 and Qwen-3 235B. We plan to release data, models, and code to accelerate future research on privacy-sensitive domains and agents.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
