One Size Fits All? A Modular Adaptive Sanitization Kit (MASK) for Customizable Privacy-Preserving Phone Scam Detection
Kangzhong Wang, Zitong Shen, Youqian Zhang, Michael MK Cheung, Xiapu Luo, Grace Ngai, Eugene Yujun Fu

TL;DR
This paper introduces MASK, a flexible framework using modular sanitization techniques to enable privacy-preserving phone scam detection with large language models, allowing personalized privacy settings without sacrificing detection accuracy.
Contribution
The work presents a novel, extensible architecture that integrates diverse sanitization methods for privacy-aware LLM-based phone scam detection, adaptable to individual user preferences.
Findings
Proposes a pluggable architecture for privacy-preserving detection.
Enables dynamic privacy adjustment based on user preferences.
Discusses modeling approaches for personalized privacy-utility trade-offs.
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…
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