The proliferation of sensors on mobile phones and wearables has led to a plethora of
context classifiers designed to sense the individual's context. We argue that a key missing …

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 …

Personal interactions using cell phones are so embedded in our everyday lives that they go
almost unnoticed. We may try to protect ourselves from releasing sensitive information by …

Smartphone applications that use passive sensing to support human health and well-being
primarily rely on:(a) generating low-dimensional representations from high-dimensional data …

Current mobile platforms provide privacy management interfaces to regulate how
applications access sensitive data. Prior research has shown how these interfaces are …

The multitude of data generated by sensors available on users' mobile devices, combined
with advances in machine learning techniques, support context-aware services in …

Modern mobile operating systems implement an ask-on-first-use policy to regulate
applications' access to private user data: the user is prompted to allow or deny access to a …

There is growing concern about how personal data are used when users grant applications
direct access to the sensors of their mobile devices. In fact, high resolution temporal data …

In this work, we explore the feasibility of using only the Bluetooth received signal strength of
a smart device to infer its user context. Devices that use Bluetooth wireless communication …

The proliferation of sensor-equipped smartphones has enabled an increasing number of
context-aware applications that provide personalized services based on users' contexts …