Collecting and analyzing massive data generated from smart devices have become
increasingly pervasive in crowdsensing, which are the building blocks for data-driven …

The widespread use of mobile devices such as smartphones, tablets, and smartwatches has
led users to constantly generate various location data during their daily activities …

In this paper, we focus on develo** a novel mechanism to preserve differential privacy in
deep neural networks, such that:(1) The privacy budget consumption is totally independent …

Nowadays, human trajectories are widely collected and utilized for scientific research and
business purpose. However, publishing trajectory data without proper handling might cause …

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework.
Many existing studies employ traditional DP mechanisms (eg, the Laplace mechanism) as …

Individuals are continually observed by an ever-increasing number of sensors that make up
the Internet of Things. The resulting streams of data, which are analyzed in real time, can …

Vehicle trajectory data is critical for traffic management and location-based services.
However, the released trajectories raise serious privacy concerns because they contain …

Sharing trajectories is beneficial for many real-world applications, such as managing
disease spread through contact tracing and tailoring public services to a population's travel …

Trajectory data has the potential to greatly benefit a wide-range of real-world applications,
such as tracking the spread of the disease through people's movement patterns and …

As smart devices and cloud services are rapidly expanding, a large amount of location
information can easily be gathered. However, there is a conflict between collecting location …