The development of Internet of Things (IoT) brings new changes to various fields.
Particularly, industrial IoT (IIoT) is promoting a new round of industrial revolution. With more …

Public intelligent services enabled by machine learning algorithms are vulnerable to model
extraction attacks that can steal confidential information of the learning models through …

Trajectory data is valuable for various applications, especially for intelligent transportation
systems, which hunger for plenty of trajectories. However, publishing trajectory data while …

Personal big data can greatly promote social management, business applications, and
personal services, and bring certain economic benefits to users. The difficulty with personal …

Privacy-preserving clustering has received increasing research attention in recent years.
Local differential privacy (LDP) is a privacy model without relying on trusted third parties. It …

In this article, we study the problem of differentially private multi-party data sharing, where
the involved parties assisted by a semi-honest curator collectively generate a shared dataset …

With the continuous development of intelligent transportation, an increasing number of smart
devices and sensors are being used to record traffic information. Recently, researchers have …

The proliferation of cloud computing has amplified the need for robust privacy-preserving
technologies, particularly when dealing with sensitive financial and human resources (HR) …

Local differential privacy (LDP for short), as an emerging standard privacy-preserving
technique that is suitable for privacy preserving data analysis, has been widely deployed …

Sparse learning is essential in mining high-dimensional data. Iterative hard thresholding
(IHT) methods are effective for optimizing nonconvex objectives for sparse learning …