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Co-location and air pollution exposure: case studies on the usefulness of location privacy

Authors:

Julius Marinak,

Ashley BangAuthors Info & Claims

LocalRec '22: Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

Article No.: 6, Pages 1 - 6

https://doi.org/10.1145/3557992.3565991

Published: 01 November 2022 Publication History

Abstract

Location privacy has long been studied, in order to protect users' location data from untrusted servers. While existing research analyzed location privacy methods with generic utility measures, the lack of application-oriented perspectives imposes challenges for adopting location privacy. This study fills the gap by putting application utility front and center, studying the impacts of location privacy in two concrete case studies. We conduct empirical evaluations with real-world datasets from two large cities, and provide in-depth analysis on the obtained results. Furthermore, we examine the relationship between generic utility and application utility as well as the trade-off between privacy and utility in specific application settings. Our results point out interesting behaviors of the studied privacy methods and can help applications with location privacy decision-making.

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Cited By

Bouros PDasu TKanza YRenz MSacharidis D(2023)LocalRec 2022 Workshop Report: The Sixth ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising Seattle, Washington, USA, November 1, 2022SIGSPATIAL Special10.1145/3632268.363227414:1(19-22)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3632268.3632274

Index Terms

Co-location and air pollution exposure: case studies on the usefulness of location privacy

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Published In

cover image ACM Conferences

LocalRec '22: Proceedings of the 6th ACM SIGSPATIAL International Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising

November 2022

47 pages

ISBN:9781450395403

DOI:10.1145/3557992

Editors:
Panagiotis Bouros
Johannes Gutenberg University Mainz, Germany
,
Tamraparni Dasu,
Yaron Kanza
AT&T Labs - Research
,
Matthias Renz
Christian-Albrechts-Universität zu Kiel, Germany
,
Dimitris Sacharidis
Université Libre de Bruxelles, Belgium

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2022

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National Science Foundation

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SIGSPATIAL '22

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SIGSPATIAL

SIGSPATIAL '22: The 30th International Conference on Advances in Geographic Information Systems

November 1, 2022

Washington, Seattle

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Cited By

Bouros PDasu TKanza YRenz MSacharidis D(2023)LocalRec 2022 Workshop Report: The Sixth ACM SIGSPATIAL Workshop on Location-Based Recommendations, Geosocial Networks and Geoadvertising Seattle, Washington, USA, November 1, 2022SIGSPATIAL Special10.1145/3632268.363227414:1(19-22)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1145/3632268.3632274

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