ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility

Arielle Moro, Benoît Garbinato

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. The 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.

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Paper Citation


in Harvard Style

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 - Volume 1: GISTAM, ISBN 978-989-758-294-3, pages 107-118. DOI: 10.5220/0006710201070118


in Bibtex Style

@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 - Volume 1: GISTAM,},
year={2018},
pages={107-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006710201070118},
isbn={978-989-758-294-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management - Volume 1: GISTAM,
TI - ResPred: A Privacy Preserving Location Prediction System Ensuring Location-based Service Utility
SN - 978-989-758-294-3
AU - Moro A.
AU - Garbinato B.
PY - 2018
SP - 107
EP - 118
DO - 10.5220/0006710201070118