The author explores the impact of real-world data priors on data reconstruction attacks, highlighting a discrepancy between theoretical models and practical outcomes. The study emphasizes the significance of incorporating data priors accurately into privacy guarantees for better alignment with real-world scenarios.
Data reconstruction attacks can be used to train models effectively with leaked data from federated learning, despite challenges in reconstruction quality and label matching.