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Authors: Arielle Moro and Benoît Garbinato

Affiliation: University of Lausanne, Switzerland

ISBN: 978-989-758-294-3

ISSN: 2184-500X

Keyword(s): Location prediction, location privacy-preserving mechanism, threat model, inference attack, location-based services

Abstract: Location prediction and location privacy has retained a lot of attention recent years. Predicting locations is the next step of Location-Based Services (LBS) because it provides information not only based on where you are but where you will be. However, obtaining information from LBS has a price for the user because she must share all her locations with the service that builds a predictive model, resulting in a loss of privacy. In this paper we propose ResPred, a system that allows LBS to request location prediction about the user. The system includes a location prediction component containing a statistical location trend model and a location privacy component aiming at blurring the predicted locations by finding an appropriate tradeoff between LBS utility and user privacy, the latter being expressed as a maximum percentage of utility loss. We evaluate ResPred from a utility/privacy perspective by comparing our privacy mechanism with existing techniques by using real user locations. T he location privacy is evaluated with an entropy-based confusion metric of an adversary during a location inference attack. The results show that our mechanism provides the best utility/privacy tradeoff and a location prediction accuracy of 60% in average for our model. (More)

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Paper citation in several formats:
Moro, A. and Garbinato, B. (2018). ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility. In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM, ISBN 978-989-758-294-3 ISSN 2184-500X, pages 107-118. DOI: 10.5220/0006710201070118

@conference{gistam18,
author={Arielle Moro. and Benoît Garbinato.},
title={ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility},
booktitle={Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM,},
year={2018},
pages={107-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006710201070118},
isbn={978-989-758-294-3},
issn={2184-500X},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - GISTAM,
TI - ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility
SN - 978-989-758-294-3
IS - 2184-500X
AU - Moro, A.
AU - Garbinato, B.
PY - 2018
SP - 107
EP - 118
DO - 10.5220/0006710201070118

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