In recent years, privacy and security concerns in machine learning have promoted trusted
federated learning to the forefront of research. Differential privacy has emerged as the de …

Mobility data captures the locations of moving objects such as humans, animals, and cars.
With the availability of Global Positioning System (GPS)–equipped mobile devices and other …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …

Trajectory analysis holds many promises, from improvements in traffic management to
routing advice or infrastructure development. However, learning users' paths is extremely …

Social recommendation refers to recommendation technology taking social relations as
additional input to improve merchandise sales and user satisfaction. It has been widely used …

The protocols that satisfy Local Differential Privacy (LDP) enable untrusted third parties to
collect aggregate information about a population without disclosing each user's privacy. In …

With the rise of smart city applications, the accessibility of users' location data by smart
devices has increased significantly. However, this poses a privacy concern as attackers can …

Streaming data, crucial for applications like crowd-sourcing analytics, behavior studies, and
real-time monitoring, faces significant privacy risks due to the large and diverse data linked …

Federated Learning (FL), a privacy-preserving training approach, has proven to be effective,
yet its vulnerability to attacks that extract information from model weights is widely …

We propose an efficient ɛ-differentially private algorithm, that given a simple weighted n-
vertex, m-edge graph G with a maximum unweighted degree Δ (G)≤ n-1, outputs a synthetic …