Protecting Private Data on Mobile Systems based on Spatio-temporal Analysis

Mobile devices such as smart phones and laptops are in common use and carry a vast amount of personal data. This paper presents an efficient behavior-based system for rapidly detecting the theft of mobile devices in order to protect the private data of their users. Our technique uses spatio-temporal information to construct models of user motion patters. These models are used to detect theft, which may produce anomalous spatio-temporal patterns. We consider two types of user models, each of which builds on the relationship between location and time of day. Our evaluation, based on the Reality Mining dataset, shows that our system is capable of detecting an attack within 15 minutes with 81% accuracy.