Differentially private stochastic gradient descent (DP-SGD) adds noise to gradients in back-
propagation, safeguarding training data from privacy leakage, particularly membership …

The Internet of Things (IoT) is transforming major industries, including but not limited to
healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually …

Industrial Internet of Things (IIoT) is revolutionizing many leading industries such as energy,
agriculture, mining, transportation, and healthcare. IIoT is a major driving force for Industry …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …

Facial recognition technologies are implemented in many areas, including but not limited to,
citizen surveillance, crime control, activity monitoring, and facial expression evaluation …

Reproducibility is crucial to the advancement of science; it strengthens confidence in
seemingly contradictory results and expands the boundaries of known discoveries …

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data
analysis, which is typically implemented either in the" central" or" local" model. The local …

Federated learning has been showing as a promising approach in paving the last mile of
artificial intelligence, due to its great potential of solving the data isolation problem in large …

Over the last decade, differential privacy (DP) has emerged as the gold standard of a
rigorous and provable privacy framework. However, there are very few practical guidelines …

In practice, differentially private data releases are designed to support a variety of
applications. A data release is fit for use if it meets target accuracy requirements for each …