De-anonymization attack on geolocated datasets

Sébastien Gambs (1) , Marc-Olivier Killijian (2) , Miguel Nuñez del Prado Cortez (2)

1 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
2 LAAS-TSF - Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique

With the advent of GPS-equipped devices, more and more geolocated datasets are being collected everyday, thus raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a mobility trace. More specifically, we propose an implementation based on a mobility model called Mobility Markov Chain (MMC). A MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design distance metrics between MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymous dataset. Moreover, experimentations conducted on real datasets show that the attack is efficient in terms of accuracy and resilient to sanitization mechanisms.

Mots clés: privacy; de-anonymization; geoprivacy

Domaines: Informatique ubiquitaire

Sébastien Gambs, Marc-Olivier Killijian, Miguel Nuñez del Prado Cortez. De-anonymization attack on geolocated datasets. The 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom-13), Jul 2013, Melbourne, Australia. 9p. ⟨hal-00718763v2⟩