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Soc. Sci. 2018, 7(10), 191; https://doi.org/10.3390/socsci7100191

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

1
Faculty of Geo-Information Science and Earth Observation (ITC), Department of Geo-information Processing, University of Twente, 7514 AE Enschede, The Netherlands
2
Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
3
Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA
*
Author to whom correspondence should be addressed.
Received: 8 August 2018 / Revised: 21 September 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
(This article belongs to the Section Human Geography and Social Sustainability)
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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. View Full-Text
Keywords: privacy; geoprivacy; geosocial network data; location-based social networks; anonymisation privacy; geoprivacy; geosocial network data; location-based social networks; anonymisation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Kounadi, O.; Resch, B.; Petutschnig, A. Privacy Threats and Protection Recommendations for the Use of Geosocial Network Data in Research. Soc. Sci. 2018, 7, 191.

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