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

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.Comment: 11 pages, 8 figure

Spatio-temporal techniques for user identification by means of GPS mobility data

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 \u201csignificant places\u201d, 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

Towards trajectory anonymization: a generalization-based approach

Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques

Time Distortion Anonymization for the Publication of Mobility Data with High Utility

An increasing amount of mobility data is being collected every day by different means, such as mobile applications or crowd-sensing campaigns. This data is sometimes published after the application of simple anonymization techniques (e.g., putting an identifier instead of the users' names), which might lead to severe threats to the privacy of the participating users. Literature contains more sophisticated anonymization techniques, often based on adding noise to the spatial data. However, these techniques either compromise the privacy if the added noise is too little or the utility of the data if the added noise is too strong. We investigate in this paper an alternative solution, which builds on time distortion instead of spatial distortion. Specifically, our contribution lies in (1) the introduction of the concept of time distortion to anonymize mobility datasets (2) Promesse, a protection mechanism implementing this concept (3) a practical study of Promesse compared to two representative spatial distortion mechanisms, namely Wait For Me, which enforces k-anonymity, and Geo-Indistinguishability, which enforces differential privacy. We evaluate our mechanism practically using three real-life datasets. Our results show that time distortion reduces the number of points of interest that can be retrieved by an adversary to under 3 %, while the introduced spatial error is almost null and the distortion introduced on the results of range queries is kept under 13 % on average.Comment: in 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Aug 2015, Helsinki, Finlan

Context Trees: Augmenting Geospatial Trajectories with Context

Exposing latent knowledge in geospatial trajectories has the potential to provide a better understanding of the movements of individuals and groups. Motivated by such a desire, this work presents the context tree, a new hierarchical data structure that summarises the context behind user actions in a single model. We propose a method for context tree construction that augments geospatial trajectories with land usage data to identify such contexts. Through evaluation of the construction method and analysis of the properties of generated context trees, we demonstrate the foundation for understanding and modelling behaviour afforded. Summarising user contexts into a single data structure gives easy access to information that would otherwise remain latent, providing the basis for better understanding and predicting the actions and behaviours of individuals and groups. Finally, we also present a method for pruning context trees, for use in applications where it is desirable to reduce the size of the tree while retaining useful information

Human Mobility Mining Using Spatio-Temporal Data

Georuumilised tehnoloogiad on lahutamatu osa meie elust: tehnoloogilise arengu ja positsioneerimiseadmete levikuga on toimunud kiire kasv kättesaadavate georuumiliste andmete mahus. Andmed kogutakse erinevate allikate kaudu, nt GPS ja mobiilseadmete logid, traadita sidevahendid ja asukohapõhised teenused ning teised positsioneerimise süsteemid. Liikumise kohta on võimalik infot koguda suures mõõtkavas ja hea täpsusega - see annab uurijatele võimaluse luua uusi ja innovaatilisi platvorme ja teenuseid georuumilise info analüüsimiseks ning parandada andmete kaevandamise ja visualiseerimise tehnikaid. Selleks, et luua hea nõustamisssüsteem, on väga oluline saada aru inimeste liikumisharjumustest ja käitumisest ning leida igapäevaste tegevuste varjatud mustrid. Magistritöö eesmärgiks on analüüsida andmekaeve meetodeid, uurides, millised mustrid võivad olla liikumise trajektoorides või milliste algoritmidega saab ennustada inimeste käitumist. Töös kontrollitakse nii olemasolevaid metoodikad ja teooriad ruumilise andmekaevandamise valdkonnas kui ka pakutakse arendatud algoritmide jada inimeste liikumise ennustamiseks. Me hindame ja vördleme tulemusi omavahel ning töötame välja metoodika inimeste liikumiskäitumise adaptiivseks andmekaevandamiseks.Geospatial technologies have become an integral part of our lives. With technological progress and rapid increase of geospatial information and inexpensive positioning technologies, more space-related data is becoming available at any time. Data is collected using multiple sources such as GPS and mobile computer logs, wireless communication devices, location-aware services and other positioning systems. This gives scientists the opportunity to create new innovative platforms for spatio-temporal data analysis and improve methods for mining and visualization for decision support. In order to provide a good decision support systems, it is vital to understand people’s movement, mobility behaviour and be able to discover hidden patterns and associations in their daily activities. The aim of this thesis is to analyze and discuss spatial data mining techniques by answering questions like what kinds of patterns can be extracted from spatio-temporal data or which methods are best for predicting human mobility behavior. In this work, we verify existing methodologies and theories about spatio-temporal data mining and propose a sequence of algorithms to achieve good human mobility prediction. We evaluate the results and propose a methodology for adaptive data mining of human mobility behavior