When two or more parties need to compute a common result while safeguarding their
sensitive inputs, they use secure multiparty computation (SMC) techniques such as garbled …

High-dimensional crowdsourced data collected from numerous users produces rich
knowledge about our society; however, it also brings unprecedented privacy threats to the …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

With rapid development of e-healthcare systems, patients that are equipped with resource-
limited e-healthcare devices (Internet of Things) generate huge amount of health data for …

The rapid advances in the Internet of Things (IoT) have promoted a revolution in
communication technology and offered various customer services. Artificial intelligence (AI) …

Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency
data collection and transmission compared with existing grids. However, this frequent data …

Federated learning enables the development of a machine learning model among
collaborating agents without requiring them to share their underlying data. However …

Recently, a number of systems have been deployed that gather sensitive statistics from user
devices while giving differential privacy guarantees. One prominent example is the …

Federated Learning is a technique that enables a large number of users to jointly learn a
shared machine learning model, managed by a centralized server, while the training data …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …