SynTraffic: A synthetic dataset for spatiotemporal human trafficking detection in heterogeneous graphs
Joao Lopes, Ana Nascimento, Pratheeksha Nair, Catalina Vajiac, Andreas Olligschlaeger, Reihaneh Rabbany, Christos Faloutsos, Mirela Cazzolato
Simpósio Brasileiro de Banco de Dados (SBBD)
Abstract
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 generation methodology, presenting a privacy-safe substitute for the real corpus.