Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy

TL;DR

This paper proposes a privacy-preserving system for early detection of online sexual predators using federated learning and differential privacy, balancing privacy concerns with detection accuracy.

Contribution

It introduces a novel privacy-preserving pipeline combining federated learning and differential privacy for predator detection, with comprehensive evaluation on real-world data.

Findings

Privacy and utility can coexist with minimal utility loss.

Various privacy-preserving implementations have different benefits and shortcomings.

The system effectively detects predators while respecting user privacy.

Abstract

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.