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Predicting and Explaining Privacy Risk Exposure in Mobility Data

Naretto, F; Pellungrini, R; Monreale, A; Nardini, FM; Musolesi, M; (2020) Predicting and Explaining Privacy Risk Exposure in Mobility Data. In: Discovery Science. DS 2020. (pp. pp. 403-418). Springer: Cham, Switzerland. Green open access

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Abstract

Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.

Type: Proceedings paper
Title: Predicting and Explaining Privacy Risk Exposure in Mobility Data
Event: International Conference on Discovery Science
ISBN-13: 9783030615260
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-61527-7_27
Publisher version: https://doi.org/10.1007/978-3-030-61527-7_27
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Privacy risk assessment, Privacy risk prediction, Explainability
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery-pp.ucl.ac.uk/id/eprint/10118301
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