Training deep neural networks (DNNs) usually requires massive training d...
In recent years, it has been claimed that releasing accurate statistical...
TREs are widely, and increasingly used to support statistical analysis o...
In federated learning (FL), a set of participants share updates computed...
We introduce a new privacy model relying on bistochastic matrices, that ...
Federated learning (FL) provides autonomy and privacy by design to
parti...
Federated learning (FL) enables learning a global machine learning model...
We review the use of differential privacy (DP) for privacy protection in...
Multiparty computation (MPC) consists in several parties engaging in joi...
The decentralized nature of federated learning, that often leverages the...
Federated learning (FL) allows a server to learn a machine learning (ML)...
Differential privacy (DP) is a neat privacy definition that can co-exist...
In our data world, a host of not necessarily trusted controllers gather ...
Anonymization for privacy-preserving data publishing, also known as
stat...
The rapid dynamics of COVID-19 calls for quick and effective tracking of...
Statistical disclosure control (SDC) was not created in a single seminal...
De Montjoye et al. claimed that most individuals can be reidentified fro...
We explore some novel connections between the main privacy models in use...
The purpose of statistical disclosure control (SDC) of microdata, a.k.a....