IoT data markets in public and private institutions have become increasingly relevant in
recent years because of their potential to improve data availability and unlock new business …

Crowdsensed Data Trading (CDT) is a novel data trading paradigm, in which each data
consumer can publicize its data demand as some crowdsensing tasks, and some mobile …

Mobile crowd sensing (MCS) is a technique where sensing tasks are outsourced to a crowd
of mobile users. Since most of sensing tasks are location-dependent, workers are required …

We develop an auction framework for privacy-preserving data aggregation in mobile
crowdsensing, where the platform plays the role as an auctioneer to recruit workers for …

Mobile crowdsensing is a new paradigm by which a platform can recruit mobile workers to
perform some sensing tasks by using their smart mobile devices. In this paper, we focus on a …

Edge servers (ESs) are utilized to achieve the storage and sharing of IoT data. However,
even if ES brings us much benefit, it also leads to many serious privacy leakage issues …

For protecting users' private data, local differential privacy (LDP) has been leveraged to
provide the privacy-preserving range query, thus supporting further statistical analysis …

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data
aggregation, incentive design, and privacy protection, simultaneously. However, existing …

The proliferation of mobile smart devices with ever improving sensing capacities means that
human-centric Mobile Crowdsensing Systems (MCSs) can economically provide a large …

In this paper, we focus on the privacy preserving mechanism design for crowdsourced
Federated Learning (FL), where a requester can outsource its model training task to some …