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The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning., , and . CoRR, (2022)Security Analysis of SplitFed Learning., , , and . SenSys, page 987-993. ACM, (2022)Towards privacy aware deep learning for embedded systems., , and . SAC, page 520-529. ACM, (2022)Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture., , , , , , and . USENIX Security Symposium, page 1433-1450. USENIX Association, (2022)Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Production Federated Learning., , , and . SP, page 1354-1371. IEEE, (2022)On the Pitfalls of Security Evaluation of Robust Federated Learning., , , and . SP (Workshops), page 57-68. IEEE, (2023)GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning., , and . CoRR, (2020)Leveraging Prior Knowledge Asymmetries in the Design of Location Privacy-Preserving Mechanisms., , , , and . CoRR, (2019)Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer., , , and . CoRR, (2019)Membership Privacy for Machine Learning Models Through Knowledge Transfer., and . AAAI, page 9549-9557. AAAI Press, (2021)