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Reconstructing Individual Data Points in Federated Learning Hardened with Differential Privacy and Secure Aggregation., , , , , and . EuroS&P, page 241-257. IEEE, (2023)Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks., , , , , and . CoRR, (2024)Introducing Model Inversion Attacks on Automatic Speaker Recognition., , , and . CoRR, (2023)Dataset Inference for Self-Supervised Models., , , , , , , and . NeurIPS, (2022)Gradient Masking and the Underestimated Robustness Threats of Differential Privacy in Deep Learning., , and . CoRR, (2021)"I Never Thought About Securing My Machine Learning Systems": A Study of Security and Privacy Awareness of Machine Learning Practitioners., , , and . MuC, page 520-546. ACM, (2021)Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees., , , , and . Proc. Priv. Enhancing Technol., 2023 (1): 158-176 (January 2023)Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders., , , , and . CoRR, (2023)Secure and Private Machine Learning.. Free University of Berlin, Germany, (2022)Applying Differential Privacy to Machine Learning: Challenges and Potentials.. Krypto-Tag, Gesellschaft für Informatik e.V. / FG KRYPTO, (2019)