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This paper introduces a framework that connects randomized smoothing to differential privacy through privacy profiles, enabling tight provable robustness guarantees against backdoor attacks that jointly affect training and inference. The approach is instantiated for DP-SGD and Deep Partition Aggregation with experiments on MNIST and CIFAR-10.