Huge amounts of unstructured data including image, video, audio, and text are ubiquitously
generated and shared, and it is a challenge to protect sensitive personal information in …

With the rapid development of low-cost consumer electronics and pervasive adoption of next
generation wireless communication technologies, a tremendous amount of data has been …

As massive data are produced from small gadgets, federated learning on mobile devices
has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) …

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

Due to the pervasiveness of always connected devices, large amounts of heterogeneous
data are continuously being collected. Beyond the benefits that accrue for the users, there …

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 …

With the advent of the era of big data, privacy issues have been becoming a hot topic in
public. Local differential privacy (LDP) is a state‐of‐the‐art privacy preservation technique …

In MCS (mobile crowd sensing), reducing network overhead, protecting IoT user privacy and
increasing the participation enthusiasm of perception task are key issues. The QLPPIA (an …

Local differential privacy (LDP) is promising for private streaming data collection and
analysis. However, existing few LDP studies over streams either apply to finite streams only …

Local differential privacy (LDP) is an emerging technique for privacy-preserving data
collection without a trusted collector. Despite its strong privacy guarantee, LDP cannot be …