Privacy Tips: Would it be ever possible to empower online social-network users to control the confidentiality of their data?
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Using the web for communication, purchases, searching information and/or socializing generates data, about ourselves, our connections and our activities, which is collected easily. In online social networks, users volunteer perhaps what is considered more personal information to their selected circles. But each person has personal preferences about what it considers public and what it considers private. The problem is that the information that is public may be used to disclose information that the users expect to remain confidential. This paper offers a path to provide tips and warnings to each user of an online social network so they can exercise control on the information they consider private not only by not disclosing such information, but by acting on their public information-items that could be informative for those information-items that are private. This is a significant challenge, because most web-applications use personalization to build a context and provide better services. We aim to raise awareness on privacy and to empower users, giving them the possibility to regulate the benefits of personalization with the privacy risks. In this paper we also show that information- items (like relationships) can be chosen as confidential, and that we can provide meaningful warnings on metrics of association and public attributes that are strong predictors of confidential information-items.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Copyright ACM, 2015. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, ISBN: 978-1-4503-3854-7, http://dx.doi.org/10.1145/2808797.2809279.
Pattern Recognition and Data Mining