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Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis.

, , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 15884-15899. PMLR, (2023)

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Defending Against Model Stealing Attacks With Adaptive Misinformation., and . CVPR, page 767-775. Computer Vision Foundation / IEEE, (2020)ArchShield: architectural framework for assisting DRAM scaling by tolerating high error rates., , and . ISCA, page 72-83. ACM, (2013)Astrea: Accurate Quantum Error-Decoding via Practical Minimum-Weight Perfect-Matching., , and . ISCA, page 2:1-2:16. ACM, (2023)Feedback-driven threading: power-efficient and high-performance execution of multi-threaded workloads on CMPs., , and . ASPLOS, page 277-286. ACM, (2008)HAMMER: boosting fidelity of noisy Quantum circuits by exploiting Hamming behavior of erroneous outcomes., , , and . ASPLOS, page 529-540. ACM, (2022)Ensemble of Diverse Mappings: Improving Reliability of Quantum Computers by Orchestrating Dissimilar Mistakes., and . MICRO, page 253-265. ACM, (2019)PrIDE: Achieving Secure Rowhammer Mitigation with Low-Cost In-DRAM Trackers., , , and . ISCA, page 1157-1172. IEEE, (2024)ImPress: Securing DRAM Against Data-Disturbance Errors via Implicit Row-Press Mitigation., , and . CoRR, (2024)Protecting DNNs from Theft using an Ensemble of Diverse Models., , and . ICLR, OpenReview.net, (2021)Randomized row-swap: mitigating Row Hammer by breaking spatial correlation between aggressor and victim rows., , , and . ASPLOS, page 1056-1069. ACM, (2022)