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Inférence et modèles de données personnelles : mobilité sociale, proximité spatiale

Roberto Pasqua 1
1 LAAS-TSF - Équipe Tolérance aux fautes et Sûreté de Fonctionnement informatique
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : The wide diffusion of smart portable devices allows the collection of a big amount of data concerning the activities of users from mobile apps. We focus our attention on location data, i.e. mobility traces, of a set of users in a crowd. Data collected from these mobile systems are studied following two axies: - Mobility models are used to simulate the behavior of users to develop opportunistic forwarding algorithms. We compare real and synthetic mobility traces to show the distance between the reality and the models. - Information on mobility may endanger the privacy of users. We analyze the impact of such information on privacy of users. The main contributions are: - We analyze the spatial and social properties of human motion from real traces collected by a highly accurate experimental localization system. - We compare the real traces with synthetic mobility traces generated from seven popular mobility models - We provide an inference algorithm based on co-location of users and we show its efficiency on different datasets. - We quantify the effect of probabilistic co-location information by means of a novel co-location attack.
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  • HAL Id : tel-01416982, version 2

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Roberto Pasqua. Inférence et modèles de données personnelles : mobilité sociale, proximité spatiale. Informatique. Université Paul Sabatier - Toulouse III, 2016. Français. ⟨NNT : 2016TOU30195⟩. ⟨tel-01416982v2⟩

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