Spatio-Temporal Techniques for User Identification by means of GPS Mobility Data

TL;DR

This paper investigates how GPS mobility data can uniquely identify individuals by analyzing the discriminatory power of movement features and proposing techniques for user identification, raising privacy concerns.

Contribution

It introduces new techniques for user identification from GPS data and demonstrates their effectiveness across multiple datasets, highlighting privacy risks.

Findings

GPS data can uniquely identify users.

Speed, direction, and travel distance are highly discriminatory.

A simple technique can identify users even with unseen location data.

Abstract

One of the greatest concerns related to the popularity of GPS-enabled devices and applications is the increasing availability of the personal location information generated by them and shared with application and service providers. Moreover, people tend to have regular routines and be characterized by a set of "significant places", thus making it possible to identify a user from his/her mobility data. In this paper we present a series of techniques for identifying individuals from their GPS movements. More specifically, we study the uniqueness of GPS information for three popular datasets, and we provide a detailed analysis of the discriminatory power of speed, direction and distance of travel. Most importantly, we present a simple yet effective technique for the identification of users from location information that are not included in the original dataset used for training, thus raising important privacy concerns for the management of location datasets.