This research addresses privacy protection in Natural Language Processing (NLP) by introducing a novel algorithm based on differential privacy, aimed at safeguarding user data in common applications such as chatbots, sentiment analysis, and machine translation. With the widespread application of NLP technology, the security and privacy protection of user data have become important issues that need to be solved urgently. This paper proposes a new privacy protection algorithm designed to effectively prevent the leakage of user sensitive information. By introducing a differential privacy mechanism, our model ensures the accuracy and reliability of data analysis results while adding random noise. This method not only reduces the risk caused by data leakage but also achieves effective processing of data while protecting user privacy. Compared to traditional privacy methods like data anonymization and homomorphic encryption, our approach offers significant advantages in terms of computational efficiency and scalability while maintaining high accuracy in data analysis. The proposed algorithm's efficacy is demonstrated through performance metrics such as accuracy (0.89), precision (0.85), and recall (0.88), outperforming other methods in balancing privacy and utility. As privacy protection regulations become increasingly stringent, enterprises and developers must take effective measures to deal with privacy risks. Our research provides an important reference for the application of privacy protection technology in the field of NLP, emphasizing the need to achieve a balance between technological innovation and user privacy. In the future, with the continuous advancement of technology, privacy protection will become a core element of data-driven applications and promote the healthy development of the entire industry.

翻译：本研究通过引入一种基于差分隐私的新算法，解决自然语言处理（NLP）中的隐私保护问题，旨在保护聊天机器人、情感分析和机器翻译等常见应用中的用户数据。随着NLP技术的广泛应用，用户数据的安全与隐私保护已成为亟待解决的重要问题。本文提出了一种新的隐私保护算法，旨在有效防止用户敏感信息泄露。通过引入差分隐私机制，我们的模型在添加随机噪声的同时，确保了数据分析结果的准确性和可靠性。该方法不仅降低了数据泄露带来的风险，还在保护用户隐私的同时实现了数据的有效处理。与数据匿名化和同态加密等传统隐私方法相比，我们的方法在计算效率和可扩展性方面具有显著优势，同时保持了数据分析的高精度。所提算法的有效性通过准确率（0.89）、精确率（0.85）和召回率（0.88）等性能指标得到验证，在平衡隐私与效用方面优于其他方法。随着隐私保护法规日益严格，企业和开发者必须采取有效措施应对隐私风险。我们的研究为隐私保护技术在NLP领域的应用提供了重要参考，强调了在技术创新与用户隐私之间实现平衡的必要性。未来，随着技术的不断进步，隐私保护将成为数据驱动应用的核心要素，并推动整个行业的健康发展。