The question of the utility of the blind peer-review system is fundamental to scientific research. Some studies investigate exactly how "blind" the papers are in the double-blind review system by manually or automatically identifying the true authors, mainly suggesting the number of self-citations in the submitted manuscripts as the primary signal for identity. However, related studies on the automated approaches are limited by the sizes of their datasets and the restricted experimental setup, thus they lack practical insights into the blind review process. In this work, we train models that identify authors, affiliations, and nationalities of the affiliations through real-world, large-scale experiments on the Microsoft Academic Graph, including the cold start scenario.