Towards Mobility Reports with User-Level Privacy
| dc.contributor.author | Kapp, Alexandra | |
| dc.contributor.author | Nuñez von Voigt, Saskia | |
| dc.contributor.author | Mihaljević, Helena | |
| dc.contributor.author | Tschorsch, Florian | |
| dc.date.accessioned | 2022-12-02T11:02:01Z | |
| dc.date.available | 2022-12-02T11:02:01Z | |
| dc.date.issued | 2022-11-21 | |
| dc.description.abstract | The importance of human mobility analyses is growing in both research and practice, especially as applications for urban planning and mobility rely on them. Aggregate statistics and visualizations play an essential role as building blocks of data explorations and summary reports, the latter being increasingly released to third parties such as municipal administrations or in the context of citizen participation. However, such explorations already pose a threat to privacy as they reveal potentially sensitive location information, and thus should not be shared without further privacy measures. There is a substantial gap between state-of-the-art research on privacy methods and their uti- lization in practice. We thus conceptualize a mobility report with differential privacy guarantees and implement it as open-source software to enable a privacy-preserving exploration of key aspects of mobility data in an easily accessible way. Moreover, we evaluate the benefits of limiting user contributions using three data sets relevant to research and practice. Our results show that even a strong limit on user contribution alters the original geospatial distribution only within a com- paratively small range, while significantly reducing the error introduced by adding noise to achieve privacy guarantees. | en |
| dc.description.sponsorship | BMBF, 01UV2090C/D, Transdisziplinäre Erforschung der Datenschutz-bewussten Verfügbarmachung von Bewegungsdaten für nachhaltige urbane Mobilität, Teilprojekt: Maschinelles Lernen | |
| dc.identifier.eissn | 1748-9733 | |
| dc.identifier.issn | 1748-9725 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/17772 | |
| dc.identifier.uri | https://doi.org/10.14279/depositonce-16560 | |
| dc.language.iso | en | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject.ddc | 005 Computerprogrammierung, Programme, Daten | de |
| dc.subject.other | human mobility data | en |
| dc.subject.other | differential privacy | en |
| dc.subject.other | user-level privacy | en |
| dc.subject.other | exploratory data analysis | en |
| dc.subject.other | mobility report | en |
| dc.title | Towards Mobility Reports with User-Level Privacy | en |
| dc.type | Article | |
| dc.type.version | acceptedVersion | |
| dcterms.bibliographicCitation.doi | 10.1080/17489725.2022.2148008 | |
| dcterms.bibliographicCitation.journaltitle | Journal of Location Based Services | |
| dcterms.bibliographicCitation.originalpublishername | Taylor & Francis | |
| dcterms.bibliographicCitation.originalpublisherplace | London [u.a.] | |
| dcterms.bibliographicCitation.pageend | 27 | |
| dcterms.bibliographicCitation.pagestart | 1 | |
| tub.accessrights.dnb | free | * |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Distributed Security Infrastructures (DSI) | |
| tub.publisher.universityorinstitution | Technische Universität Berlin |