Modern cyber physical systems (CPSs) has widely being used in our daily lives because of
development of information and communication technologies (ICT). With the provision of …

One of the biggest concerns of big data is privacy. However, the study on big data privacy is
still at a very early stage. We believe the forthcoming solutions and theories of big data …

The objective of differential privacy (DP) is to protect privacy by producing an output
distribution that is indistinguishable between any two neighboring databases. However …

Differential privacy is an essential and prevalent privacy model that has been widely
explored in recent decades. This survey provides a comprehensive and structured overview …

High-dimensional crowdsourced data collected from numerous users produces rich
knowledge about our society; however, it also brings unprecedented privacy threats to the …

With the continuous improvement of hardware computing power, edge computing of
industrial data has been gradually applied. In the past decade, the promotion of edge …

Shortly after it was first introduced in 2006, differential privacy became the flagship data
privacy definition. Since then, numerous variants and extensions were proposed to adapt it …

With the popularity of smart devices, multi-access edge computing (MEC) has become the
mainstream of dealing with big data in heterogeneous Internet of Things (H-IoT). MEC …

Differential privacy (DP) is a widely accepted mathematical framework for protecting data
privacy. Simply stated, it guarantees that the distribution of query results changes only …

Differential privacy has been a de facto privacy standard in defining privacy and handling
privacy preservation. It has had great success in scenarios of local data privacy and …