Temporal fingerprints for identity matching across fully encrypted domains

In the digital age, coordinated inauthentic behavior threatens societal stability, markets, and security. Advances in generative AI amplify these threats, enabling effortless content creation, amplifying actors' influence. Detection is hindered by cross-domain activity, where pseudonymous profiles operate across encrypted platforms, and by privacy constraints limiting content analysis. In this study, we propose a robust and scalable cross-domain identity matching framework, based on bursty dynamics, independent of content or interaction data. It outperforms state-of-the-art temporal and structural approaches, remains resilient to incomplete data, and correctly identifies 35% of profiles after 52 weeks. It scales effectively, attaining AUC 0.78 when matching identities across 500 marketplaces with over 250k daily traders. By framing identity matching within the "network of networks" perspective, we demonstrate how coordinated behavior propagates across domains. This dual methodological and theoretical contribution paves the way for innovative strategies to combat digital threats in an increasingly complex and adversarial landscape.