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Computational Concentration of Measure: Optimal Bounds, Reductions, and More., , and . SODA, page 345-363. SIAM, (2020)Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture., , , , , , and . USENIX Security Symposium, page 1433-1450. USENIX Association, (2022)Is Private Learning Possible with Instance Encoding?, , , , , , , and . SP, page 410-427. IEEE, (2021)Property Inference from Poisoning., , and . SP, page 1120-1137. IEEE, (2022)Model-Targeted Poisoning Attacks with Provable Convergence., , , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 10000-10010. PMLR, (2021)Learning under p-tampering poisoning attacks., , and . Ann. Math. Artif. Intell., 88 (7): 759-792 (2020)Computational Concentration of Measure: Optimal Bounds, Reductions, and More, , and . (2019)cite arxiv:1907.05401.Polynomial-Time Targeted Attacks on Coin Tossing for Any Number of Corruptions., , , and . TCC (2), volume 13043 of Lecture Notes in Computer Science, page 718-750. Springer, (2021)Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning., , , , and . NeurIPS, (2022)Effectively Using Public Data in Privacy Preserving Machine Learning., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 25718-25732. PMLR, (2023)