Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2013

Abstract

As the amount of content users publish on social networking sites rises, so do the danger and costs of inadvertently sharing content with an unintended audience. Studies repeatedly show that users frequently misconfigure their policies or misunderstand the privacy features offered by social networks. A way to mitigate these problems is to develop automated tools to assist users in correctly setting their policy. This paper explores the viability of one such approach: we examine the extent to which machine learning can be used to deduce users' sharing preferences for content posted on Facebook. To generate data on which to evaluate our approach, we conduct an online survey of Facebook users, gathering their Facebook posts and associated policies, as well as their intended privacy policy for a subset of the posts. We use this data to test the efficacy of several algorithms at predicting policies, and the effects on prediction accuracy of varying the features on which they base their predictions. We find that Facebook's default behavior of assigning to a new post the privacy settings of the preceding one correctly assigns policies for only 67% of posts. The best of the prediction algorithms we tested outperforms this baseline for 80% of participants, with an average accuracy of 81%; this equates to a 45% reduction in the number of posts with misconfigured policies. Further, for those participants (66%) whose implemented policy usually matched their intended policy, our approach predicts the correct privacy settings for 94% of posts. © 2013 ACM.

Keywords

Facebook, machine learning, natural language processing, privacy, social network

Discipline

Computer Sciences | Numerical Analysis and Scientific Computing | Social Media

Publication

AISec '13: Proceedings of the 2013 ACM workshop on Artificial Intelligence and Security, Berlin, November 4

First Page

13

Last Page

23

ISBN

9781450324885

Identifier

10.1145/2517312.2517317

Publisher

ACM

City or Country

New York

Copyright Owner and License

Publisher / LARC

Additional URL

https://doi.org/10.1145/2517312.2517317

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