Information about us, our actions, and our preferences is created at scale through surveys or
scientific studies or as a result of our interaction with digital devices such as smartphones …

Collaborative networks and data sharing initiatives are broadening the opportunities for the
advancement of science. These initiatives offer greater transparency in science, with the …

In a modern power system with an increasing proportion of renewable energy, wind power
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …

There is growing interest in enhancing research transparency and reproducibility in
economics and other scientific fields. We survey existing work on these topics within …

Differential privacy is a recent framework for computation on sensitive data, which has
shown considerable promise in the regime of large datasets. Stochastic gradient methods …

Differential privacy is a powerful tool for providing privacy-preserving noisy query answers
over statistical databases. It guarantees that the distribution of noisy query answers changes …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

Federated machine learning (FL) allows to collectively train models on sensitive data as only
the clients' models and not their training data need to be shared. However, despite the …

Private companies, government entities, and institutions such as hospitals routinely gather
vast amounts of digitized personal information about the individuals who are their …