Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy
This addresses privacy concerns in detecting sexual predators online, particularly for children, but is incremental as it combines existing techniques.
The paper tackled the problem of detecting online grooming of children by implementing a privacy-preserving pipeline using federated learning and differential privacy, achieving coexistence of privacy and utility with only a slight reduction in utility.
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.