The interest in trajectory data has sensibly increased since the widespread of mobile devices. Simple clustering techniques allow the recognition of personal gazetteers, i.e., the set of main points of interest (also called stay points) of each user, together with the list of time instants of each visit. Due to their sensitiveness, personal gazetteers are usually anonymized, but their inherent unique patterns expose them to the risk of being de-anonymized. In particular, social trajectories (i.e., those obtained from social networks, which associate statuses and check-ins to spatial and temporal locations) can be leveraged by an adversary to de-anonymize personal gazetteers. In this paper, we propose DART as an innovative approach to effectively de-anonymize personal gazetteers through social trajectories, even in the absence of a temporal alignment between the two sources (i.e., they have been collected over different periods). DART relies on a big data implementation, guaranteeing the scalability to large volumes of data. We evaluate our approach on two real-world datasets and we compare it with recent state-of-the-art algorithms to verify its effectiveness.

DART: De-Anonymization of personal gazetteers through social trajectories

Francia M.;Gallinucci E.;Golfarelli M.;Santolini N.
2020

Abstract

The interest in trajectory data has sensibly increased since the widespread of mobile devices. Simple clustering techniques allow the recognition of personal gazetteers, i.e., the set of main points of interest (also called stay points) of each user, together with the list of time instants of each visit. Due to their sensitiveness, personal gazetteers are usually anonymized, but their inherent unique patterns expose them to the risk of being de-anonymized. In particular, social trajectories (i.e., those obtained from social networks, which associate statuses and check-ins to spatial and temporal locations) can be leveraged by an adversary to de-anonymize personal gazetteers. In this paper, we propose DART as an innovative approach to effectively de-anonymize personal gazetteers through social trajectories, even in the absence of a temporal alignment between the two sources (i.e., they have been collected over different periods). DART relies on a big data implementation, guaranteeing the scalability to large volumes of data. We evaluate our approach on two real-world datasets and we compare it with recent state-of-the-art algorithms to verify its effectiveness.
Francia M.; Gallinucci E.; Golfarelli M.; Santolini N.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/789303
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