279 citations · 1.4k across the 46 of their papers we have counts for
4 papers · 1 filter
Analyzing Accuracy Loss in Randomized Smoothing Defenses
Yue Gao, Harrison Rosenberg, Kassem Fawaz +2
Recent advances in machine learning (ML) algorithms, especially deep neural networks (DNNs), have demonstrated remarkable success (sometimes exceeding human-level performance) on s…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Hypothesis Testing Interpretations and Renyi Differential Privacy
Borja Balle, Gilles Barthe, Marco Gaboardi +2
Differential privacy is a de facto standard in data privacy, with applications in the public and private sectors. A way to explain differential privacy, which is particularly appea…
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Yuzhe Ma, Xiaojin Zhu, Justin Hsu
Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set. We consider differential privacy as a defensive mea…