Logo des Repositoriums
 

D-TOUR: Detour-based point of interest detection in privacy-sensitive trajectories

Kurzanzeige
dc.contributor.authorSchneider,Maja
dc.contributor.authorGehrke,Lukas
dc.contributor.authorChristen,Peter
dc.contributor.authorRahm,Erhard
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:18Z
dc.date.available2022-09-28T17:10:18Z
dc.date.issued2022
dc.description.abstractData collected through mobile sensors on private and commercial devices can give valuable insights into mobility patterns and facilitate applications such as urban planning or traffic forecasting. At the same time, such data can carry immense privacy risks for the data producers. Stop detection approaches can reveal a person's points of interest (POI) by clustering temporal and spatial features, uncovering private attributes such as home or work addresses. Privacy-preserving mechanisms aim at hiding these POIs, for example via speed smoothing approaches that are able to preserve high data utility. We show experimentally on two real-world data sets that trajectories can contain anomalies that are contained to a certain extent when smoothing a route and are not detected by state of the art stop detection algorithms. We propose a novel attack D-TOUR that reveals POIs based on deviations from the optimal route. Our experiments suggest that our proposed attack has similar performance on unprotected data but outperforms the baseline approach, especially when protection is higher and route features become more sparse.en
dc.identifier.doi10.18420/inf2022_20
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39518
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectLocation Privacy
dc.subjectTrajectory data
dc.subjectInference attack
dc.subjectPoints of interest
dc.titleD-TOUR: Detour-based point of interest detection in privacy-sensitive trajectoriesen
gi.citation.endPage230
gi.citation.startPage219
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitle3. Interdisciplinary Privacy & Security at Large Workshop (Privacy&Security@Large)

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild
Name:
secprivatlarge_06.pdf
Größe:
267.41 KB
Format:
Adobe Portable Document Format
Herunterladen

Sammlungen

P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften