Ontology-Based Modeling of Privacy Vulnerabilities for Data Sharing

Jens Hjort Schwee*, Fisayo Caleb Sangogboye, Aslak Johansen, Mikkel Baun Kjærgaard

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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Abstract

When several parties want to share sensor-based datasets it can be difficult to know exactly what kinds of information can be extracted from the shared data. This is because many types of sensor data can be used to estimate indirect information, e.g., in smart buildings a CO2 stream can be used to estimate the presence and number of occupants in each room. If a data publisher does not consider these transformations of data their privacy protection of the data might be problematic. It currently requires a manual inspection by a knowledge expert of each dataset to identify possible privacy vulnerabilities for estimating indirect information. This manual process does not scale with the increasing availability of data due to the general lack of experts and the associated cost with their work. To improve this process, we propose a privacy vulnerability ontology that helps highlight the specific privacy challenges that can emerge when sharing a dataset. The ontology is intended to model data transformations, privacy attacks, and privacy risks regarding data streams. In the paper, we have used the ontology for modeling the findings of eight papers in the smart building domain. Furthermore, the ontology is applied to a case study scenario using a published dataset. The results show that the ontology can be used to highlight privacy risks in datasets.
Original languageEnglish
Title of host publicationPrivacy and Identity Management. Data for Better Living : AI and Privacy
EditorsMichael Friedewald, Melek Önen, Eva Lievens, Stephan Krenn, Samuel Fricker
Volume576
PublisherSpringer
Publication dateMar 2020
Pages109-125
ISBN (Print)978-3-030-42503-6
ISBN (Electronic)978-3-030-42504-3
DOIs
Publication statusPublished - Mar 2020
Event14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School - Windisch, Windisch, Switzerland
Duration: 19. Aug 201923. Aug 2019
Conference number: 14

Seminar

Seminar14th IFIP WG 9.2, 9.6/11.7, 11.6/SIG 9.2.2 International Summer School
Number14
LocationWindisch
Country/TerritorySwitzerland
CityWindisch
Period19/08/201923/08/2019
SeriesIFIP Advances in Information and Communication Technology
ISSN1571-5736

Keywords

  • Open data
  • Data anonymization
  • Modeling methodologies
  • Data publishing
  • Data privacy
  • Privacy-Preserving Data Publishing

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