用联邦学习与差分隐私检测网络诱拐，保护孩子隐私同时保持高准确率。
Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
- 在本地设备训练模型，不上传原始对话数据。
- 实测隐私保护下检测准确率仅微降，效果仍可靠。
- 适合关注儿童网络安全与隐私保护的研究者。
新冠疫情导致屏幕时间增加和社交隔离，致使网络诱拐案件显著上升。以往的检测方法依赖中心化模型访问或上传私密对话，存在隐私风险。本文构建了一套隐私保护的早期检测流程，结合联邦学习与差分隐私技术，在不集中存储敏感数据的前提下实现对性诱骗行为的识别。通过真实世界数据的广泛评估表明，该方法在保障隐私的同时，仅带来轻微的性能损失，证明隐私与模型效用可共存。
原文摘要 · Abstract (English)
The increased screen time and isolation caused by the COVID-19 pandemic have led to a significant surge in cases of online grooming, which is the use of strategies by predators to lure children into sexual exploitation. Previous efforts to detect grooming in industry and academia have involved accessing and monitoring private conversations through centrally-trained models or sending private conversations to a global server. In this work, we implement a privacy-preserving pipeline for the early detection of sexual predators. We leverage federated learning and differential privacy in order to create safer online spaces for children while respecting their privacy. We investigate various privacy-preserving implementations and discuss their benefits and shortcomings. Our extensive evaluation using real-world data proves that privacy and utility can coexist with only a slight reduction in utility.
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