基于差分隐私和联邦学习的隐私金融文本分类
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning
摘要：
考虑到金融领域，隐私很重要，因为这些数据是高度机密和敏感的。自然语言处理（NLP）技术可以应用于金融领域的文本分类和实体检测目的，例如客户反馈情感分析、发票实体检测、按类型对金融文档进行分类等。由于此类数据的敏感性，需要采取隐私措施来处理和训练具有此类数据的大型模型。在这项工作中，我们提出了一个上下文化的Transformer（BERT和RoBERTa）的文本分类模型集成的隐私功能，如差分隐私（DP）和联邦学习（FL）。我们介绍了如何私下训练NLP模型和理想的隐私-效用权衡，并在金融短语银行数据集上对其进行评估。
英文摘要：
Privacy is important considering the financial Domain as such data is highly confidential and sensitive. Natural Language Processing (NLP) techniques can be applied for text classification and entity detection purposes in financial domains such as customer feedback sentiment analysis, invoice entity detection, categorisation of financial documents by type etc. Due to the sensitive nature of such data, privacy measures need to be taken for handling and training large models with such data. In this work, we propose a contextualized transformer (BERT and RoBERTa) based text classification model integrated with privacy features such as Differential Privacy (DP) and Federated Learning (FL). We present how to privately train NLP models and desirable privacy-utility tradeoffs and evaluate them on the Financial Phrase Bank dataset.
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。
