Many subfields of machine learning share a common stumbling block: evaluation. Advances
in machine learning often evaporate under closer scrutiny or turn out to be less widely …

Privacy-preserving statistical data analysis addresses the general question of protecting
privacy when publicly releasing information about a sensitive dataset. A privacy attack takes …

We propose a scheme for auditing differentially private machine learning systems with a
single training run. This exploits the parallelism of being able to add or remove multiple …

Adversarial examples that fool machine learning models, particularly deep neural networks,
have been a topic of intense research interest, with attacks and defenses being developed …

Machine learning algorithms, when applied to sensitive data, pose a distinct threat to
privacy. A growing body of prior work demonstrates that models produced by these …

Machine learning techniques based on neural networks are achieving remarkable results in
a wide variety of domains. Often, the training of models requires large, representative …

Data deletion algorithms aim to remove the influence of deleted data points from trained
models at a cheaper computational cost than fully retraining those models. However, for …

Membership inference determines, given a sample and trained parameters of a machine
learning model, whether the sample was part of the training set. In this paper, we derive the …

Abstract “Concentrated differential privacy” was recently introduced by Dwork and Rothblum
as a relaxation of differential privacy, which permits sharper analyses of many privacy …

We develop and study multicalibration as a new measure of fairness in machine learning
that aims to mitigate inadvertent or malicious discrimination that is introduced at training time …