Objective Differential privacy is a relatively new method for data privacy that has seen
growing use due its strong protections that rely on added noise. This study assesses the …

Wearable devices generate different types of physiological data about the individuals. These
data can provide valuable insights for medical researchers and clinicians that cannot be …

With rapid development of e-healthcare systems, patients that are equipped with resource-
limited e-healthcare devices (Internet of Things) generate huge amount of health data for …

As an emerging training model, federated deep learning has been widely applied in many
fields such as speech recognition, image classification and classification of peer-to-peer …

With the rapid development of the Internet of Things (IoT), wearable devices have become
ubiquitous and interconnected in daily lives. Because wearable devices collect, transmit …

Data privacy, especially location privacy, is paramountly important for protecting individual's
information in smart cities in the big data era. One of the examples is in spatial …

The rapid development of the information-centric wireless sensor network (ICWSN) has
solved the challenges of information transmission and processing caused by the …

In this paper, we propose a user-friendly encrypted storage scheme named EStore, which is
based on the Hadoop distributed file system. Users can make use of cloud-based distributed …

Federated Learning is a valuable instrument for building AI-based systems that preserve the
privacy and security of sensitive data, based on the main concept of shifting no more the …

Large amounts of time-series data need to be continually delivered from IoT devices to the
cloud for real-time data analytics. The data delivery process is intrinsically slow and costly …