SynTraffic: A synthetic dataset for spatiotemporal human trafficking detection in heterogeneous graphs - Lopes, João; Nascimento, Ana; Nair, Pratheeksha; Vajiac, Catalina; Olligschlaeger, Andreas; Rabbany, Reihaneh; Faloutsos, Christos; Cazzolato, Mirela (Anais Estendidos do Simpósio Brasileiro de Banco de Dados (SBBD), 2026)
Online escort platforms have become a common vector for human trafficking (HT) networks to advertise victims alongside legitimate listings, making manual screening infeasible. Progress on automated analysis is limited by restricted data access: HT advertisement corpora carry personal, potentially victim-identifying information, imposing ethical and legal constraints on sharing and reproducibility. This work addresses that gap with a reproducible methodology for generating synthetic HT advertisement data that is statistically representative of a real corpus, without exposing sensitive content. The proposed dataset SynTraffic reproduces posting volume, spatial mobility, temporal dynamics, and metadata-sharing structure, including the size distribution of identity clusters formed by shared contact metadata. We statistically validate SynTraffic testing distributional fidelity and cluster-size fidelity. SynTraffic is openly available, as well as the the generation methodology, presenting a privacy-safe substitute for the real corpus.