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Perceived Versus Actual Predictability of Personal Information in Social Networks

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Internet Science (INSCI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9934))

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Abstract

This paper looks at the problem of privacy in the context of Online Social Networks (OSNs). In particular, it examines the predictability of different types of personal information based on OSN data and compares it to the perceptions of users about the disclosure of their information. To this end, a real life dataset is composed. This consists of the Facebook data (images, posts and likes) of 170 people along with their replies to a survey that addresses both their personal information, as well as their perceptions about the sensitivity and the predictability of different types of information. Importantly, we evaluate several learning techniques for the prediction of user attributes based on their OSN data. Our analysis shows that the perceptions of users with respect to the disclosure of specific types of information are often incorrect. For instance, it appears that the predictability of their political beliefs and employment status is higher than they tend to believe. Interestingly, it also appears that information that is characterized by users as more sensitive, is actually more easily predictable than users think, and vice versa (i.e. information that is characterized as relatively less sensitive is less easily predictable than users might have thought).

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Notes

  1. 1.

    https://databait.hwcomms.com.

  2. 2.

    http://usemp-mklab.iti.gr/usemp/prepilot_survey_data_statistics.pdf.

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Acknowledgment

This work is supported by the USEMP FP7 project, partially funded by the EC under contract number 611596.

Author information

Authors and Affiliations

  1. CERTH-ITI, Thessaloniki, Greece

    Eleftherios Spyromitros-Xioufis, Georgios Petkos, Symeon Papadopoulos & Yiannis Kompatsiaris

  2. iMinds-SMIT, Vrije Universiteit Brussel, Brussels, Belgium

    Rob Heyman

Authors
  1. Eleftherios Spyromitros-Xioufis
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  2. Georgios Petkos
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  3. Symeon Papadopoulos
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  4. Rob Heyman
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  5. Yiannis Kompatsiaris
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Corresponding author

Correspondence to Eleftherios Spyromitros-Xioufis .

Editor information

Editors and Affiliations

  1. University of Florence, Florence, Italy

    Franco Bagnoli

  2. Center for Research and Technology, Thessaloniki, Greece

    Anna Satsiou

  3. National and Kapodistrian University, Athens, Greece

    Ioannis Stavrakakis

  4. University of Florence, Florence, Italy

    Paolo Nesi

  5. University of Florence, Florence, Italy

    Giovanna Pacini

  6. University of Zurich, Zürich, Switzerland

    Yanina Welp

  7. University of Southampton, Southampton, United Kingdom

    Thanassis Tiropanis

  8. University of Southampton, Southampton, United Kingdom

    Dominic DiFranzo

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Spyromitros-Xioufis, E., Petkos, G., Papadopoulos, S., Heyman, R., Kompatsiaris, Y. (2016). Perceived Versus Actual Predictability of Personal Information in Social Networks. In: Bagnoli, F., et al. Internet Science. INSCI 2016. Lecture Notes in Computer Science(), vol 9934. Springer, Cham. https://doi.org/10.1007/978-3-319-45982-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-45982-0_13

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  • Online ISBN: 978-3-319-45982-0

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