This paper focuses on the way to protect privacy of clients requesting datasets stored in data
servers while keeping communication efficiency. To this end, we introduce a novel …

Mobile CrowdSensing (MCS) is a new paradigm that leverages pervasive mobile devices to
efficiently collect the big sensory data, enabling various large-scale applications. However …

Various paradigms, based on differential privacy, have been proposed to release a privacy-
preserving dataset with statistical approximation. Nonetheless, most existing schemes are …

Mobile crowdsensing is growing in popularity by collecting environmental information from
participants' mobile phones. However, the sensing data may carry sensitive information of …

With the evolution of conventional VANETs (Vehicle Ad-hoc Networks) into the IoV (Internet
of Vehicles), vehicle-based spatial crowdsourcing has become a potential solution for …

In many mobile applications, user-generated data are presented as set-valued data. To
tackle potential privacy threats in analyzing these valuable data, local differential privacy has …

On-device machine learning (ML) has brought about the accessibility to a tremendous
amount of data from the users while keeping their local data private instead of storing it in a …

Mobile crowdsensing (MCS) utilizes ubiquitous mobile devices to collect massive amounts
of data and offer various high-quality services. During the data collection and upload …

Although releasing crowdsourced data brings many benefits to the data analyzers to
conduct statistical analysis, it may violate crowd users' data privacy. A potential way to …

The paradigm of crowd-sourced data collection (also known as participatory sensing) has
been bolstered by the extensive availability of on-board sensors and electronic devices in …