Identification of Social Aspects by Means of Inertial Sensor Data

Today’s applications and providers are very interested in knowing the social aspects of users in order to customize the services they provide and to be more effective. Among the others, the most frequented places and the paths to reach them are information that turns out to be very useful to define users’ habits. The most exploited means to acquire positions and paths is the GPS sensor, however it has been shown how leveraging inertial data from installed sensors can lead to path identification. In this work, we present a Computationally Efficient algorithm to Reconstruct Vehicular Traces (CERT), a novel algorithm which computes the path traveled by a vehicle using accelerometer and magnetometer data. We show that by analyzing data obtained through the accelerometer and the magnetometer in vehicular scenarios, CERT achieves almost perfect identification for medium and small sized cities. Moreover, we show that the longer the path, the easier it is to recognize it. We also present results characterizing the privacy risks depending on the area of the world, since, as we show, urban dynamics play a key role in the path detection.