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Making machine learning robust against adversarial inputs., , and . Commun. ACM, 61 (7): 56-66 (2018)On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches., , , , , , , and . CoRR, (2017)The Space of Transferable Adversarial Examples., , , , and . CoRR, (2017)Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data., , , , and . ICLR, OpenReview.net, (2017)A Marauder's Map of Security and Privacy in Machine Learning.. CoRR, (2018)cleverhans v0.1: an adversarial machine learning library., , and . CoRR, (2016)Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks., , , , and . CoRR, (2015)Machine Unlearning., , , , , , , and . CoRR, (2019)Private Multi-Winner Voting for Machine Learning., , , , , , , , and . Proc. Priv. Enhancing Technol., 2023 (1): 527-555 (January 2023)Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings., , , and . CoRR, (2020)