Big data drive multidimensional convergence and profound innovations among industries
and provide novel ways of exploring the world. As they significantly create economic and …

Mobile crowdsensing enables convenient sensory data collection from a large number of
mobile devices and has found various applications. In the real practice, however, the …

In this paper, we propose a lightweight privacy-preserving Faster R-CNN framework
(SecRCNN) for object detection in medical images. Faster R-CNN is one of the most …

We survey distributed deep learning models for training or inference without accessing raw
data from clients. These methods aim to protect confidential patterns in data while still …

Privacy leakage is a serious issue in spatial crowdsourcing in various scenarios. In this
paper, we study privacy protection in spatial crowdsourcing. The main challenge is to …

Truth discovery in mobile crowdsensing has recently received wide attention. It refers to the
procedure for estimating the unknown user reliability from collected sensory data and …

E-Healthcare systems are increasingly popular due to the introduction of wearable
healthcare devices and sensors. Personal health records (PHRs) are collected by these …

Nowadays, cloud computing provides a platform infrastructure for the secure dealing of
digital data, but privacy and copy control are the two important issues in it over a network …

In the era of Industrial Internet of Things (IIoT), huge amounts of data are generated, and
companies are highly motivated to store the data on cloud servers for cost saving and …

Outsourcing machine learning inference services to the cloud is getting increasingly
popular. However, this also entails privacy risks to the provider's proprietary model and the …