Evaluating potential sensitive information leaks on a smartphone using the magnetometer and Conformal Prediction

Smartphones are part of our daily lives, they allow the users to conduct online transactions, take photos and even play games. To support and enrich the user experience, sensors have been added to enable the detection of the context of a user's interaction with the phone. For example, by detecting the phone's orientation through the sensor readings, app developers may adjust the screen to portrait or landscape for a better viewing experience; or switching the screen off to avoid unintended touches when the phone is near the face using the proximity sensor; or adjusting the screen brightness using the ambient light sensor. However, despite being widely used in many mobile apps, these low powered sensors do not require any permissions from the user, and are potential targets for side channel attacks. Hackers can design apps that harvest the sensor readings to infer the user's activities. Detecting such malicious use is a difficult task for Machine Learning and often results in many unwanted false positives. Therefore, in this paper, we implement Conformal Prediction to detect potentially sensitive information being leaked via the magnetometer. We will assess the validity and accuracy of our algorithm in three different real-world scenarios.