Recent advances in telecommunications and database systems have allowed the scientific
community to efficiently mine vast amounts of information worldwide and to extract new …

With the rapid development of mobile medical care, medical institutions also have the
hidden danger of privacy leakage while sharing personal medical data. Based on the k …

In this article, we propose a trustworthy privacy-preserving federated learning (FL)-based
deep learning (DL) service framework for Industrial Internet of Things-enabled systems. FL …

For the extraction of useful patterns, the collected data should be distributed to and shared
with analyzers. This, however, creates problems and challenges for the individual with …

The rapid development of the mobile Internet coupled with the widespread use of intelligent
terminals have intensified the digitization of personal information and accelerated the …

Social Networks have seen an exponential increase in their user activity. The corresponding
user data can be extremely valuable, for improving user experience, identifying trends to …

Privacy is a significant issue that requires consideration in all applications. Data collected
from various individuals and organizations must be disclosed to the public or private parties …

One of the main steps in the data lifecycle is to publish it for data analysts to discover hidden
patterns. But, data publishing may lead to unwanted disclosure of personal information and …

With the rapid development of social networks, privacy-preserving is an important issue.
Nowadays, privacy-preserving mainly involves static social networks. In fact, social networks …

Trajectory data generated in location-based service environments contain highly sensitive
personal information, making them a prime target for privacy attacks. At the same time …