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Adversarial interference and its mitigations in privacy-preserving collaborative machine learning.

, , , , , , , and . Nat. Mach. Intell., 3 (9): 749-758 (2021)

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Unified Interpretation of the Gaussian Mechanism for Differential Privacy Through the Sensitivity Index., , , , , and . J. Priv. Confidentiality, (2022)Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training., , and . EICC, page 179-185. ACM, (2024)Membership Inference Attacks Against Semantic Segmentation Models., , and . AISec@CCS, page 43-53. ACM, (2023)Adversarial interference and its mitigations in privacy-preserving collaborative machine learning., , , , , , , and . Nat. Mach. Intell., 3 (9): 749-758 (2021)Beyond Gradients: Exploiting Adversarial Priors in Model Inversion Attacks., , and . ACM Trans. Priv. Secur., 26 (3): 38:1-38:30 (August 2023)Naturally Private Recommendations with Determinantal Point Processes., , , and . CoRR, (2024)Privacy-preserving medical image analysis., , , , , , , , , and 1 other author(s). CoRR, (2020)Differentially Private Graph Neural Networks for Whole-Graph Classification., , , , , and . IEEE Trans. Pattern Anal. Mach. Intell., 45 (6): 7308-7318 (June 2023)Can Collaborative Learning Be Private, Robust and Scalable?, , , , and . DeCaF/FAIR@MICCAI, volume 13573 of Lecture Notes in Computer Science, page 37-46. Springer, (2022)