A privacy-aware model to process data from location-based social media

Lade...
Vorschaubild
Dateien
Loechner_2-1tc0wl382uqkr0.pdf
Loechner_2-1tc0wl382uqkr0.pdfGröße: 3.63 MBDownloads: 277
Datum
2018
Autor:innen
Löchner, Marc
Dunkel, Alexander
Burghardt, Dirk
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Green
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Publikationstyp
Beitrag zu einem Konferenzband
Publikationsstatus
Published
Erschienen in
BURGHARDT, Dirk, ed., Siming CHEN, ed., Gennady ANDRIENKO, ed., Natalia ANDRIENKO, ed., Ross PURVES, ed., Alexandra DIEHL, ed.. VGI Geovisual Analytics Workshop. 2018
Zusammenfassung

Many social media services offer their users to add location data to their posts. Since this data is usually publicly available, it can be used to create thematic maps based on the topical information, e.g. derived from hashtags attached to posts. However, users might not be aware, that their publications can be used for other purposes by third parties. In certain situations it can be compromising the user’s privacy. We introduce a conceptual model to help people who create those maps to preserve privacy of social media users. Therefore we analyze the data in a set of four facets. For each facet, we eliminate precise data by deriving multiple abstraction layers from it. Using these layers, we are able to quantitatively describe different levels of privacy. We further describe an example application to show use cases for the abstraction model on the same data in two contrary scenarios.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Privacy, Social Computing, Geospatial Analysis
Konferenz
VGI Geovisual Analytics Workshop, colocated with BDVA 2018, 19. Okt. 2018, Konstanz, Germany
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690LÖCHNER, Marc, Alexander DUNKEL, Dirk BURGHARDT, 2018. A privacy-aware model to process data from location-based social media. VGI Geovisual Analytics Workshop, colocated with BDVA 2018. Konstanz, Germany, 19. Okt. 2018. In: BURGHARDT, Dirk, ed., Siming CHEN, ed., Gennady ANDRIENKO, ed., Natalia ANDRIENKO, ed., Ross PURVES, ed., Alexandra DIEHL, ed.. VGI Geovisual Analytics Workshop. 2018
BibTex
@inproceedings{Lochner2018priva-43921,
  year={2018},
  title={A privacy-aware model to process data from location-based social media},
  booktitle={VGI Geovisual Analytics Workshop},
  editor={Burghardt, Dirk and Chen, Siming and Andrienko, Gennady and Andrienko, Natalia and Purves, Ross and Diehl, Alexandra},
  author={Löchner, Marc and Dunkel, Alexander and Burghardt, Dirk}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43921">
    <dc:rights>terms-of-use</dc:rights>
    <dc:contributor>Dunkel, Alexander</dc:contributor>
    <dcterms:issued>2018</dcterms:issued>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/43921/3/Loechner_2-1tc0wl382uqkr0.pdf"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/43921"/>
    <dc:creator>Burghardt, Dirk</dc:creator>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-11-20T11:45:13Z</dcterms:available>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">Many social media services offer their users to add location data to their posts. Since this data is usually publicly available, it can be used to create thematic maps based on the topical information, e.g. derived from hashtags attached to posts. However, users might not be aware, that their publications can be used for other purposes by third parties. In certain situations it can be compromising the user’s privacy. We introduce a conceptual model to help people who create those maps to preserve privacy of social media users. Therefore we analyze the data in a set of four facets. For each facet, we eliminate precise data by deriving multiple abstraction layers from it. Using these layers, we are able to quantitatively describe different levels of privacy. We further describe an example application to show use cases for the abstraction model on the same data in two contrary scenarios.</dcterms:abstract>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:contributor>Burghardt, Dirk</dc:contributor>
    <dcterms:title>A privacy-aware model to process data from location-based social media</dcterms:title>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2018-11-20T11:45:13Z</dc:date>
    <dc:creator>Dunkel, Alexander</dc:creator>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/43921/3/Loechner_2-1tc0wl382uqkr0.pdf"/>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:creator>Löchner, Marc</dc:creator>
    <dc:contributor>Löchner, Marc</dc:contributor>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Ja
Begutachtet
Diese Publikation teilen