Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

How much does a machine learning algorithm leak about its training data, and why?
Membership inference attacks are used as an auditing tool to quantify this leakage. In this …

Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair
machine learning aims at minimizing discrimination against protected groups by, for …

Graph structured data have enabled several successful applications such as
recommendation systems and traffic prediction, given the rich node features and edges …

Deploying machine learning models in production may allow adversaries to infer sensitive
information about training data. There is a vast literature analyzing different types of …

We consider the privacy-preserving machine learning (ML) setting where the trained model
must satisfy differential privacy (DP) with respect to the labels of the training examples. We …

Abstract Algorithms such as Differentially Private SGD enable training machine learning
models with formal privacy guarantees. However, because these guarantees hold with …

Differential privacy (DP) is by far the most widely accepted framework for mitigating privacy
risks in machine learning. However, exactly how small the privacy parameter $\epsilon …

Federated learning is a decentralized machine learning approach where clients train
models locally and share model updates to develop a global model. This enables low …

Unprecedented quantities of data that could help social scientists understand and
ameliorate the challenges of human society are presently locked away inside companies …