loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Arielle Moro and Benoît Garbinato

Affiliation: University of Lausanne, Switzerland

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. 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 216.73.216.231

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Moro, A., Garbinato and 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, SciTePress, 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
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic