Application of a predictive method to protect privacy of mobility data - Multidisciplinary Institute in Artificial intelligence - Grenoble Alpes
Journal Articles Control Engineering Practice Year : 2025

Application of a predictive method to protect privacy of mobility data

Abstract

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.
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hal-04885266 , version 1 (14-01-2025)

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Emilio Molina, Mirko Fiacchini, Arthur Goarant, Rémy Raes, Sophie Cerf, et al.. Application of a predictive method to protect privacy of mobility data. Control Engineering Practice, 2025, 156, pp.106223. ⟨10.1016/j.conengprac.2024.106223⟩. ⟨hal-04885266⟩
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