To apply federated learning to drug discovery we developed a novel platform in the context
of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n 831472) …

We propose a versatile privacy framework for quantum systems, termed quantum pufferfish
privacy (QPP). Inspired by classical pufferfish privacy, our formulation generalizes and …

Pufferfish privacy (PP) is a generalization of differential privacy (DP), that offers flexibility in
specifying sensitive information and integrates domain knowledge into the privacy definition …

Existing research in differential privacy, whose applications have exploded across functional
areas in the last few years, describes an intrinsic trade-off between the privacy of a dataset …

Singular value decomposition (SVD) is a fundamental technique widely used in various
applications, such as recommendation systems and principal component analyses. In recent …

This paper introduces two methods of creating differentially private (DP) synthetic data that
are now incorporated into the synth pop package for R. Both are suitable for synthesising …

Federated learning is increasingly popular as it enables multiple parties with limited
datasets and resources to train a high-performing machine learning model collaboratively …

We introduce “Natural” differential privacy (NDP)—which utilizes features of existing
hardware architecture to implement differentially private computations. We show that NDP …

Quantile regression (QR) is a powerful and robust statistical modeling method broadly used
in many fields such as economics, ecology, and healthcare. However, it has not been well …

Differentially Private Stochastic Gradient Descent (DP-SGD) is a key method for applying
privacy in the training of deep learning models. This applies isotropic Gaussian noise to …