PANORAMA：一个用于研究大语言模型敏感数据记忆问题的合成含PII数据集
PANORAMA: A synthetic PII-laced dataset for studying sensitive data memorization in LLMs
AI总结：
针对现有敏感数据记忆研究缺乏优质数据集的问题，研究团队构建了含38万余样本的合成PII数据集PANORAMA，通过微调实验验证了其效用，为LLM隐私研究提供关键资源。
摘要：
随着大语言模型（LLM）规模不断扩大且在现实应用中部署愈发广泛，其对敏感信息及个人可识别信息（PII）的记忆行为带来的隐私风险日益凸显。目前针对敏感数据与PII记忆问题的研究以及缓解策略的开发工作，因缺乏能反映网络上敏感信息多样性的全面、真实且符合伦理来源的数据集而受到阻碍。\n我们推出PANORAMA——全称为Profile-based Assemblage for Naturalistic Online Representation and Attribute Memorization Analysis（基于个人档案的自然化在线表征与属性记忆分析集合），这是一个包含384789个样本的大规模合成语料库，源自9674个合成个人档案，旨在高度模拟在线环境中自然存在的PII与敏感数据的分布、类型及上下文场景。\n我们的数据生成流程首先通过约束选择构建内部一致的多属性人类档案，以反映教育、健康属性、财务状况等现实人口统计特征。随后结合零样本提示与OpenAI o3-mini，生成多种类型的内容——包括维基风格文章、社交媒体帖子、论坛讨论、在线评论、留言以及市场商品列表——每类内容都嵌入了真实、符合上下文语境的PII及其他敏感信息。\n为验证PANORAMA的实用性，我们使用数据子集以1倍、5倍、10倍和25倍的数据重复率对Mistral-7B模型进行微调，并测量PII记忆率——结果不仅显示记忆率随重复次数持续上升，还存在内容类型间的差异，凸显了PANORAMA能够建模不同上下文场景下记忆风险的差异。我们的数据集与代码已公开，为隐私风险评估、模型审计以及隐私保护型LLM的开发提供了亟需的资源。
英文摘要：
The memorization of sensitive and personally identifiable information (PII) by large language models (LLMs) poses growing privacy risks as models scale and are increasingly deployed in real-world applications. Existing efforts to study sensitive and PII data memorization and develop mitigation strategies are hampered by the absence of comprehensive, realistic, and ethically sourced datasets reflecting the diversity of sensitive information found on the web. We introduce PANORAMA - Profile-based Assemblage for Naturalistic Online Representation and Attribute Memorization Analysis, a large-scale synthetic corpus of 384,789 samples derived from 9,674 synthetic profiles designed to closely emulate the distribution, variety, and context of PII and sensitive data as it naturally occurs in online environments. Our data generation pipeline begins with the construction of internally consistent, multi-attribute human profiles using constrained selection to reflect real-world demographics such as education, health attributes, financial status, etc. Using a combination of zero-shot prompting and OpenAI o3-mini, we generate diverse content types - including wiki-style articles, social media posts, forum discussions, online reviews, comments, and marketplace listings - each embedding realistic, contextually appropriate PII and other sensitive information. We validate the utility of PANORAMA by fine-tuning the Mistral-7B model on 1x, 5x, 10x, and 25x data replication rates with a subset of data and measure PII memorization rates - revealing not only consistent increases with repetition but also variation across content types, highlighting PANORAMA's ability to model how memorization risks differ by context. Our dataset and code are publicly available, providing a much-needed resource for privacy risk assessment, model auditing, and the development of privacy-preserving LLMs.
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