Application of a predictive method to protect privacy of mobility data

Molina, Emilio; Fiacchini, Mirko; Goarant, Arthur; Raes, Rémy; Cerf, Sophie; Robu, Bogdan

Type de document :

Article dans une revue scientifique: Article original

DOI :

10.1016/j.conengprac.2024.106223

Titre :

Application of a predictive method to protect privacy of mobility data

Auteur(s) :

Molina, Emilio [Auteur]
GIPSA - Modelling and Optimal Decision for Uncertain Systems [GIPSA-MODUS]
Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé [CREATIS]
Fiacchini, Mirko [Auteur]
GIPSA - Modelling and Optimal Decision for Uncertain Systems [GIPSA-MODUS]
Goarant, Arthur [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Raes, Rémy [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Cerf, Sophie [Auteur]
Self-adaptation for distributed services and large software systems [SPIRALS]
Robu, Bogdan [Auteur]
GIPSA - Modelling and Optimal Decision for Uncertain Systems [GIPSA-MODUS]

Titre de la revue :

Control Engineering Practice

Pagination :

106223

Éditeur :

Elsevier

Date de publication :

2025-03

ISSN :

0967-0661

Mot(s)-clé(s) en anglais :

Optimal privacy
MPC
Location privacy

Discipline(s) HAL :

Sciences de l'ingénieur [physics]/Automatique / Robotique
Informatique [cs]

Résumé en anglais : [en]

Users of geo-localized applications on mobile devices need protection to avoid threats to their privacy. Such protection should vary in time, to cope with the dynamical nature of mobility data. We present a method to protect ...
Lire la suite >Users of geo-localized applications on mobile devices need protection to avoid threats to their privacy. Such protection should vary in time, to cope with the dynamical nature of mobility data. We present a method to protect the privacy of users of location-based services, based on Model Predictive Control techniques. We employ three different predictors for future movements: an exact predictor, which serves as the baseline for the best expected performance, and two additional predictors allowing for online implementation. One of these predictors assumes the user is moving in a way that minimizes privacy, while the other is a linear predictor. The method has been applied to two datasets, Privamov and Cabspotting, which contain mobility data collected from real users when using a mobile device. The method demonstrated an improvement in privacy compared to a state-of-the-art mechanism by approximately 12% increase for Privamov users and 5% for Cabspotting users, while maintaining the same level of utility.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Systemes et Algorithmes Pervasifs au confluent des mondes physique et numérique
MIAI @ Grenoble Alpes

Collections :