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Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research

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  • Ourania Kounadi

    (Faculty of Geo-Information Science and Earth Observation (ITC), Department of Geo-information Processing, University of Twente, 7514 AE Enschede, The Netherlands
    Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria)

  • Bernd Resch

    (Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
    Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA)

  • Andreas Petutschnig

    (Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria)

Abstract

Inference attacks and protection measures are two sides of the same coin. Although the former aims to reveal information while the latter aims to hide it, they both increase awareness regarding the risks and threats from social media apps. On the one hand, inference attack studies explore the types of personal information that can be revealed and the methods used to extract it. An additional risk is that geosocial media data are collected massively for research purposes, and the processing or publication of these data may further compromise individual privacy. On the other hand, consistent and increasing research on location protection measures promises solutions that mitigate disclosure risks. In this paper, we examine recent research efforts on the spectrum of privacy issues related to geosocial network data and identify the contributions and limitations of these research efforts. Furthermore, we provide protection recommendations to researchers that share, anonymise, and store social media data or publish scientific results.

Suggested Citation

  • Ourania Kounadi & Bernd Resch & Andreas Petutschnig, 2018. "Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research," Social Sciences, MDPI, vol. 7(10), pages 1-17, October.
    • Handle: RePEc:gam:jscscx:v:7:y:2018:i:10:p:191-:d:175043
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    References listed on IDEAS

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    1. Anna Kovacs-Gyori & Alina Ristea & Clemens Havas & Bernd Resch & Pablo Cabrera-Barona, 2018. "#London2012: Towards Citizen-Contributed Urban Planning Through Sentiment Analysis of Twitter Data," Urban Planning, Cogitatio Press, vol. 3(1), pages 75-99.
    2. Bernd Resch & Anja Summa & Peter Zeile & Michael Strube, 2016. "Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm," Urban Planning, Cogitatio Press, vol. 1(2), pages 114-127.
    3. William Lee Croft & Wei Shi & Jörg-Rüdiger Sack & Jean-Pierre Corriveau, 2017. "Comparison of approaches of geographic partitioning for data anonymization," Journal of Geographical Systems, Springer, vol. 19(3), pages 221-248, July.
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    Cited by:

    1. Katerina Vgena & Angeliki Kitsiou & Christos Kalloniatis & Stefanos Gritzalis, 2022. "Determining the Role of Social Identity Attributes to the Protection of Users’ Privacy in Social Media," Future Internet, MDPI, vol. 14(9), pages 1-18, August.
    2. Masahiko Haraguchi & Akihiko Nishino & Akira Kodaka & Maura Allaire & Upmanu Lall & Liao Kuei-Hsien & Kaya Onda & Kota Tsubouchi & Naohiko Kohtake, 2022. "Human mobility data and analysis for urban resilience: A systematic review," Environment and Planning B, , vol. 49(5), pages 1507-1535, June.
    3. Bernd Resch & Inga Puetz & Matthias Bluemke & Kalliopi Kyriakou & Jakob Miksch, 2020. "An Interdisciplinary Mixed-Methods Approach to Analyzing Urban Spaces: The Case of Urban Walkability and Bikeability," IJERPH, MDPI, vol. 17(19), pages 1-20, September.
    4. Gonzalo Wandosell & María Concepción Parra-Meroño & Raul Baños, 2019. "Online Store Locator: An Essential Resource for Retailers in the 21st Century," Social Sciences, MDPI, vol. 8(2), pages 1-13, February.

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