PPM: A Privacy Prediction Model for Online Social Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)

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.

Keywords

Privacy prediction Decision making Online Social Networks (OSNs) 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Cailing Dong
    • 1
  • Hongxia Jin
    • 2
  • Bart P. Knijnenburg
    • 3
  1. 1.Department of Information SystemsUniversity of Maryland, Baltimore CountyBaltimoreUSA
  2. 2.Samsung Research AmericaMountain ViewUSA
  3. 3.Human-Centered ComputingClemson UniversityClemsonUSA

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