A Framework for Computing the Privacy Scores of Users in Online Social Networks
By Kun Liu, Evimaria Terzi. 2009 Ninth IEEE International Conference on Data Mining, 2009
295 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/95e0eee803fdeba2f5ffacad2daaae44e12d113b
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Citations in the corpus
- Privacy wizards for social networking sites (2010, background): “In a similar vein, recent work has proposed a methodology for quantifying the risk posed by a user’s privacy settings [30, 28]; at a high level, a risk score communicates to a user the extent to which his privacy settings differ from those of other users who are close to him in the social network gr”
- A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects (2013, background): “Recently, there have been many new research studies in this area, such as anti-spam [69], [70] in social media and access control [71] and privacy preservation [72]–[74] in (online) social networks.”
- Technical Privacy Metrics (2015, background): “10 Privacy score Social networks [84] 5.”