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PPM: A Privacy Prediction Model for Online Social Networks

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10047))

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Abstract

Online Social Networks (OSNs) have come to play an increasingly important role in our social lives, and their inherent privacy problems have become a major concern for users. Can we assist consumers in their privacy decision-making practices, for example by predicting their preferences and giving them personalized advice? To this end, we introduce PPM: a Privacy Prediction Model, rooted in psychological principles, which can be used to give users personalized advice regarding their privacy decision-making practices. Using this model, we study psychological variables that are known to affect users’ disclosure behavior: the trustworthiness of the requester/information audience, the sharing tendency of the receiver/information holder, the sensitivity of the requested/shared information, the appropriateness of the request/sharing activities, as well as several more traditional contextual factors.

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Notes

  1. 1.

    https://www.mturk.com/mturk/.

  2. 2.

    https://developers.google.com/places/.

  3. 3.

    As contextual factors are not studied in the friend requests scenario, we have no results for removing such factors from \(D_{Twitter}\) and \(D_{Google+}\). Similarly, although trustworthiness of the audience is an important factor in the location sharing study, our privacy decision making model is built for audience separately (as these measures are repeated per scenario). Thus, trustworthiness is not a feature in \(D_{Family}\), \(D_{Friend}\) and \(D_{Colleague}\). Finally, the features belonging to appropriateness are difficult to split off from contextual factors in the location study, so those results are combined.

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Authors and Affiliations

  1. Department of Information Systems, University of Maryland, Baltimore County, Baltimore, USA

    Cailing Dong

  2. Samsung Research America, Mountain View, USA

    Hongxia Jin

  3. Human-Centered Computing, Clemson University, Clemson, USA

    Bart P. Knijnenburg

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  1. Cailing Dong
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  2. Hongxia Jin
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Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Dong, C., Jin, H., Knijnenburg, B.P. (2016). PPM: A Privacy Prediction Model for Online Social Networks. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_28

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