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A Semi-supervised Approach to Measuring User Privacy in Online Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9956))

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Abstract

During our digital social life, we share terabytes of information that can potentially reveal private facts and personality traits to unexpected strangers. Despite the research efforts aiming at providing efficient solutions for the anonymization of huge databases (including networked data), in online social networks the most powerful privacy protection is in the hands of the users. However, most users are not aware of the risks derived by the indiscriminate disclosure of their personal data. With the aim of fostering their awareness on private data leakage risk, some measures have been proposed that quantify the privacy risk of each user. However, these measures do not capture the objective risk of users since they assume that all user’s direct social connections are close (thus trustworthy) friends. Since this assumption is too strong, in this paper we propose an alternative approach: each user decides which friends are allowed to see each profile item/post and our privacy score is defined accordingly. We show that it can be easily computed with minimal user intervention by leveraging an active learning approach. Finally, we validate our measure on a set of real Facebook users.

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Notes

  1. 1.

    Source: http://www.alexa.com/.

  2. 2.

    https://research.facebook.com/blog/three-and-a-half-degrees-of-separation/.

  3. 3.

    http://www.zdnet.com/article/facebook-cto-most-people-have-modified-their- privacy-settings/.

  4. 4.

    http://www.consumerreports.org/cro/magazine/2012/06/facebook-your-privacy/index.htm.

  5. 5.

    http://www.pewresearch.org/fact-tank/2014/02/03/6-new-facts-about-facebook/.

  6. 6.

    http://kdd.di.unito.it/privacyawareness/.

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Acknowledgments

The work presented in this paper has been co-funded by Fondazione CRT (grant number 2015-1638). The authors wish to thank all the volunteers who participated in the survey.

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Authors and Affiliations

  1. Department of Computer Science, University of Torino, Turin, Italy

    Ruggero G. Pensa & Gianpiero Di Blasi

Authors
  1. Ruggero G. Pensa
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  2. Gianpiero Di Blasi
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Correspondence to Ruggero G. Pensa .

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Editors and Affiliations

  1. Campus Middelhe, M.G.103a, Universiteit Antwerpen Campus Middelhe, M.G.103a, Antwerp, Belgium

    Toon Calders

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Bari, Italy

    Donato Malerba

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Pensa, R.G., Di Blasi, G. (2016). A Semi-supervised Approach to Measuring User Privacy in Online Social Networks. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_25

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  • DOI: https://doi.org/10.1007/978-3-319-46307-0_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46306-3

  • Online ISBN: 978-3-319-46307-0

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