从社交媒体中自动提取PII以提高隐私意识：一种深度迁移学习方法
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach
摘要：
互联网用户在社交媒体上暴露了越来越多的个人身份信息（PII）。此类暴露的PII可能会给用户造成严重损失，告知用户他们的PII暴露对于提高他们的隐私意识并鼓励他们采取保护措施至关重要。为此，需要先进的自动化技术。虽然信息抽取（IE）技术可用于自动提取PII，但基于深度学习（DL）的IE模型减轻了对特征工程的需求并进一步提高了效率。然而，基于DL的IE模型通常需要大规模的标记数据进行训练，但由于隐私问题，PII标记的社交媒体帖子很难获得。此外，这些模型严重依赖于预先训练的词嵌入，而社交媒体中的PII通常形式不同，因此在预先训练的词嵌入中没有固定的表示。在这项研究中，我们提出了用于PII提取的深度迁移学习（DTL-PIIE）框架来解决这两个限制。DTL-PIIE将从公开的PII数据中学到的知识转移到社交媒体，以解决罕见的PII标记数据的问题。此外，我们的框架利用图卷积网络（GCN）来整合语法模式来指导PIIE，而不依赖于预先训练的词嵌入。对基准IE模型的评估表明，我们的方法优于国家的最先进的DL为基础的IE模型。我们的框架可以促进各种应用，如PII误用预测和隐私风险评估，保护互联网用户的隐私。
英文摘要：
Internet users have been exposing an increasing amount of Personally Identifiable Information (PII) on social media. Such exposed PII can cause severe losses to the users, and informing users of their PII exposure is crucial to raise their privacy awareness and encourage them to take protective measures. To this end, advanced automatic techniques are needed. While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency. However, DL-based IE models often require large-scale labeled data for training, but PII-labeled social media posts are difficult to obtain due to privacy concerns. Also, these models rely heavily on pre-trained word embeddings, while PII in social media often varies in forms and thus has no fixed representations in pre-trained word embeddings. In this study, we propose the Deep Transfer Learning for PII Extraction (DTL-PIIE) framework to address these two limitations. DTL-PIIE transfers knowledge learned from publicly available PII data to social media to address the problem of rare PII-labeled data. Moreover, our framework leverages Graph Convolutional Networks (GCNs) to incorporate syntactic patterns to guide PIIE without relying on pre-trained word embeddings. Evaluation against benchmark IE models indicates that our approach outperforms state-of-the-art DL-based IE models. Our framework can facilitate various applications, such as PII misuse prediction and privacy risk assessment, protecting the privacy of internet users.
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。
