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Modeling the Impact of Privacy on Information Diffusion in Social Networks

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Complex Networks VIII (CompleNet 2017)

Part of the book series: Springer Proceedings in Complexity ((SPCOM))

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Abstract

Humans like to disseminate ideas and news, as proved by the huge success of online social networking platforms such as Facebook or Twitter. On the other hand, these platforms have emphasized the dark side of information spreading, such as the diffusion of private facts and rumors in the society. Fortunately, in some cases, online social network users can set a level of privacy and decide to whom to show their information. However, they cannot control how their friends will use this information. The behavior of each user depends on her attitude toward privacy, that has a crucial role in the way information propagates across the network. With the aim of providing a mathematical tool for measuring the exposure of networks to privacy leakage risks, we extend the classic Susceptible-Infectious-Recovered (SIR) epidemic model in order to take the privacy attitude of users into account. We leverage such model to measure the contribution of the privacy attitude of each individual to the robustness of the whole network to the spread of personal information, depending on its structure and degree distribution. We study experimentally our model by means of stochastic simulations on four synthetic networks generated with classical algorithms.

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Notes

  1. 1.

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

  2. 2.

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

  3. 3.

    Thus, in our model, the edges are directed from the source of the information to its target.

  4. 4.

    https://github.com/ldbc/ldbc_snb_datagen.

  5. 5.

    Such information is known by social network providers.

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Acknowledgements

This work was supported by Fondazione CRT (grant number 2015-1638).

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Authors and Affiliations

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

    Livio Bioglio & Ruggero G. Pensa

Authors
  1. Livio Bioglio
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  2. Ruggero G. Pensa
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Corresponding author

Correspondence to Livio Bioglio .

Editor information

Editors and Affiliations

  1. New York University, Center for Data Science New York University, New York, New York, USA

    Bruno Gonçalves

  2. BioComplex Laboratory, School of Computing, Florida Institute of Technology, Melbourne, Florida, USA

    Ronaldo Menezes

  3. Center for Network Science and Mathematics Department, Central European University, Budapest, Hungary

    Roberta Sinatra

  4. Rudjer Bošković Institute, Theoretical Physics Division, Zagreb, Croatia

    Vinko Zlatic

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Bioglio, L., Pensa, R.G. (2017). Modeling the Impact of Privacy on Information Diffusion in Social Networks. In: Gonçalves, B., Menezes, R., Sinatra, R., Zlatic, V. (eds) Complex Networks VIII. CompleNet 2017. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-54241-6_8

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