在加密文本上训练自然语言处理模型以增强隐私性
Training Natural Language Processing Models on Encrypted Text for Enhanced Privacy
AI总结：
针对云环境下NLP模型训练的数据隐私问题，提出在加密文本上训练模型的方法，经Doc2Vec+XGBoost、Doc2Vec+LSTM两种架构在20 Newsgroups数据集上验证，可在不损失准确率的同时保障隐私，并提供复现用Colab notebook。
摘要：
随着基于云的服务在机器学习模型训练与部署中的应用日益广泛，数据隐私已成为一大核心关切。这对于自然语言处理（NLP）模型而言尤为重要，因为这类模型通常会处理个人通信、机密文件等敏感信息。本研究提出一种在加密文本数据上训练NLP模型的方法，旨在缓解数据隐私顾虑，同时保持与在非加密数据上训练的模型相近的性能。我们采用两种不同架构——即Doc2Vec+XGBoost和Doc2Vec+LSTM——验证该方法，并在20 Newsgroups数据集上评估模型性能。结果表明，加密与非加密模型的性能相当，说明我们的加密方法可在不牺牲模型准确率的前提下有效保护数据隐私。为便于复现实验，我们提供了Colab notebook，地址为：https://t.ly/lR-TP
英文摘要：
With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP
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。
