Phone scams remain a pervasive threat to both personal safety and financial security worldwide. Recent advances in large language models (LLMs) have demonstrated strong potential in detecting fraudulent behavior by analyzing transcribed phone conversations. However, these capabilities introduce notable privacy risks, as such conversations frequently contain sensitive personal information that may be exposed to third-party service providers during processing. In this work, we explore how to harness LLMs for phone scam detection while preserving user privacy. We propose MASK (Modular Adaptive Sanitization Kit), a trainable and extensible framework that enables dynamic privacy adjustment based on individual preferences. MASK provides a pluggable architecture that accommodates diverse sanitization methods - from traditional keyword-based techniques for high-privacy users to sophisticated neural approaches for those prioritizing accuracy. We also discuss potential modeling approaches and loss function designs for future development, enabling the creation of truly personalized, privacy-aware LLM-based detection systems that balance user trust and detection effectiveness, even beyond phone scam context.

翻译：电话诈骗仍然是全球范围内对人身安全和金融安全的普遍威胁。大型语言模型（LLM）的最新进展通过分析转录的电话对话，在检测欺诈行为方面展现出巨大潜力。然而，这些能力也带来了显著的隐私风险，因为此类对话通常包含敏感个人信息，在处理过程中可能暴露给第三方服务提供商。在本研究中，我们探讨如何在利用LLM进行电话诈骗检测的同时保护用户隐私。我们提出了MASK（模块化自适应脱敏套件），这是一个可训练且可扩展的框架，能够基于个人偏好进行动态隐私调整。MASK提供了一个可插拔的架构，支持多种脱敏方法——从适用于高隐私需求用户的传统基于关键词的技术，到适用于优先考虑准确性的用户的复杂神经方法。我们还讨论了未来开发的潜在建模方法和损失函数设计，使得能够创建真正个性化、具备隐私意识的基于LLM的检测系统，在用户信任与检测效能之间取得平衡，其应用甚至可超越电话诈骗检测的范畴。