Recent developments in a variety of sectors, including health care, research and the direct-
to-consumer industry, have led to a dramatic increase in the amount of genomic data that …

The sharing of genomic data holds great promise in advancing precision medicine and
providing personalized treatments and other types of interventions. However, these …

Today, computer systems hold large amounts of personal data. Yet while such an
abundance of data allows breakthroughs in artificial intelligence, and especially machine …

Deep neural networks are susceptible to various inference attacks as they remember
information about their training data. We design white-box inference attacks to perform a …

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 models leak significant amount of information about their training sets,
through their predictions. This is a serious privacy concern for the users of machine learning …

Membership inference (MI) attacks exploit the fact that machine learning algorithms
sometimes leak information about their training data through the learned model. In this work …

Machine-learning (ML) algorithms are increasingly utilized in privacy-sensitive applications
such as predicting lifestyle choices, making medical diagnoses, and facial recognition. In a …

Secure aggregation consists of computing the sum of data collected from multiple sources
without disclosing these individual inputs. Secure aggregation has been found useful for …

Today's systems produce a rapidly exploding amount of data, and the data further derives
more data, forming a complex data propagation network that we call the data's lineage …