Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

Objective Differential privacy is a relatively new method for data privacy that has seen
growing use due its strong protections that rely on added noise. This study assesses the …

Linear regression is an important tool across many fields that work with sensitive human-
sourced data. Significant prior work has focused on producing differentially private point …

Background Artificial intelligence (AI) has been successfully applied in numerous scientific
domains. In biomedicine, AI has already shown tremendous potential, eg, in the …

The influence of artificial intelligence (AI) has drastically risen in recent years, especially in
the field of medicine. Its influence has spread so greatly that it is determined to become a …

Differential privacy guarantees allow the results of a statistical analysis involving sensitive
data to be released without compromising the privacy of any individual taking part …

Precision medicine has become a novel and rising concept, which depends much on the
identification of individual genomic signatures for different patients. The cancer cell lines …

Generalized linear models (GLMs) such as logistic regression are among the most widely
used arms in data analyst's repertoire and often used on sensitive datasets. A large body of …

Many data producers seek to provide users access to confidential data without unduly
compromising data subjects' privacy and confidentiality. One general strategy is to require …

Many applications of machine learning, for example in health care, would benefit from
methods that can guarantee privacy of data subjects. Differential privacy (DP) has become …