UC Berkeley

Millions of smartphones are stolen in the United States every year, putting victims' personal information at risk since many users often do not lock their phones. To protect individuals' smartphones and the private data stored on them, we developed a system that automatically detects pickpocket and grab-and-run theft, in which a thief grabs the phone from a victim's hand then runs away. Our system applies machine learning to smartphone accelerometer data in order to detect possible theft incidents. Based on a field study and simulated theft scenarios, we are able to detect all thefts at a cost of 1 false alarm per week. Given that many smartphone users refuse to enable screen locking mechanisms over complaints that it takes too long to unlock their devices, our system could be used in conjunction with these systems in order to drastically decrease the number of times a user is asked to provide a lock code. That is, our system could be used to prompt smartphone users for PINs or passcodes only when theft events have been detected.