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Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups.

, , , , , , , and . Sensors, 23 (1): 31 (2023)

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User Label Leakage from Gradients in Federated Learning., , , , , , , , and . CoRR, (2021)Datenschutzfreundliche Erkennung und Abwehr von Insiderbedrohungen.. University of Hamburg, Germany, (2020)Using Context and Provenance to defend against USB-borne attacks., , and . ARES, page 1:1-1:9. ACM, (2019)Label-Aware Aggregation for Improved Federated Learning., , , , , , and . FMEC, page 216-223. IEEE, (2023)On the Effectiveness of Intersection Attacks in Anonymous Microblogging., , , , and . NordSec, volume 13700 of Lecture Notes in Computer Science, page 3-19. Springer, (2022)Hashing of personally identifiable information is not sufficient., , , , and . Sicherheit, volume P-281 of LNI, page 55-68. Gesellschaft für Informatik e.V., (2018)PEEPLL: privacy-enhanced event pseudonymisation with limited linkability., , , and . SAC, page 1308-1311. ACM, (2020)User-Level Label Leakage from Gradients in Federated Learning., , , , , , , , and . Proc. Priv. Enhancing Technol., 2022 (2): 227-244 (2022)Mitigating Intersection Attacks in Anonymous Microblogging., , , , and . ARES, page 8:1-8:11. ACM, (2023)PEEPLL: Privacy-Enhanced Event Pseudonymisation with Limited Linkability., , , and . CoRR, (2019)