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 …

The CyBOK project would like to understand how the CyBOK is being used and its uptake.
The project would like organisations using, or intending to use, CyBOK for the purposes of …

Differential privacy provides a formal approach to privacy of individuals. Applications of
differential privacy in various scenarios, such as protecting users' original utterances, must …

Interpretability is often pointed out as a key requirement for trustworthy machine learning.
However, learning and releasing models that are inherently interpretable leaks information …

Training generative models to produce synthetic data is meant to provide a privacy-friendly
approach to data release. However, we get robust guarantees only when models are trained …

Although query-based systems (QBS) have become one of the main solutions to share data
anonymously, building QBSes that robustly protect the privacy of individuals contributing to …

Data privacy is a central theme in the global dialogue around the application of data science
in education. Despite the growing need, research organisations and private companies …

In recent years, a growing body of work has emerged on how to learn machine learning
models under fairness constraints, often expressed with respect to some sensitive attributes …

While there were already some privacy challenges organized in the domain of data
sanitization, they have mainly focused on the defense side of the problem. To favor the …

Preserving privacy of users is a key requirement of web-scale analytics and reporting
applications, and has witnessed a renewed focus in light of recent data breaches and new …