Title: One Size Fits All? A Modular Adaptive Sanitization Kit (MASK) for Customizable Privacy-Preserving Phone Scam Detection Show Chinese title

Title: 一种尺寸适合所有？ 一种模块化自适应净化工具包（MASK）用于可定制的隐私保护电话诈骗检测

Abstract: 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. Show Chinese abstract

Abstract: 电话诈骗仍然是全球范围内对个人安全和财务安全的普遍威胁。 近年来，大型语言模型（LLMs）在分析转录的电话对话中检测欺诈行为方面展示了强大的潜力。 然而，这些能力引入了显著的隐私风险，因为在处理过程中，此类对话通常包含可能被暴露给第三方服务提供商的敏感个人信息。 在本工作中，我们探讨了如何在保护用户隐私的同时利用LLMs进行电话诈骗检测。 我们提出了MASK（模块化自适应净化工具包），这是一个可训练且可扩展的框架，可根据个人偏好动态调整隐私设置。 MASK提供了一个可插拔的架构，可以容纳多种净化方法——从传统的基于关键词的技术，适用于高隐私需求的用户，到复杂的神经方法，适用于更注重准确性的用户。 我们还讨论了未来发展的潜在建模方法和损失函数设计，使创建真正个性化的、具有隐私意识的LLM驱动的检测系统成为可能，即使在电话诈骗之外的场景中也能平衡用户信任和检测效果。