With the widespread development of the quantified-self movement, an increasing number of
users rely on mobile applications to monitor their physical activity through their smartphones …

The rapid deployment of sensor systems in homes and work environments, and new
applications of machine learning at the edge have posed an enormous and unprecedented …

Personal sensory data is used by context-aware mobile applications to provide utility.
However, the same data can also be used by an adversary to make sensitive inferences …

Recent advances in computing have allowed for the possibility to collect large amounts of
data on personal activities and private living spaces. Collecting and publishing a dataset in …

An increasing number of sensors on mobile, Internet of things (IoT), and wearable devices
generate time-series measurements of physical activities. Though access to the sensory …

Sharing ubiquitous mobile sensor data, especially physiological data, raises potential risks
of leaking physical and demographic information that can be inferred from the time series …

Deep neural networks have been successfully applied to activity recognition with wearables
in terms of recognition performance. However, the black-box nature of neural networks could …

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely
used in today's applications and services. However, with social awareness of privacy and …

Public release of wrist-worn motion sensor data is growing. They enable and accelerate
research in developing new algorithms to passively track daily activities, resulting in …

Utilities around the world are reported to invest a total of around $30 billion over the next few
years for installation of more than 300 million smart meters, replacing traditional analog …