Analyzing facebook privacy settings: user expectations vs. reality
By Yabing Liu, Krishna P. Gummadi, Balachander Krishnamurthy, Alan Mislove. ACM/SIGCOMM Internet Measurement Conference, 2011
601 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/740f4097ca7011a542766f35f2e9bd8064ca30b5
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In Artificial Intelligence
In Sociology and Political Science
Citations in the corpus
- Privacy.tag: privacy concern expressed and respected (2014, background): “Do people have to relinquish their privacy in the era of mobile and wearable computing?”
- Privacy Concerns and Behaviors of People with Visual Impairments (2015, background): “One challenge they mentioned is the notoriously volatile and complicated nature of Face-book’s privacy settings [8, 20].”
- Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images (2017, neutral): “Other tasks include preserving one’s privacy while using social networks [15, 52, 27] and exploring privacy settings [11, 8, 28].”
- Exploring the Security of Information Sharing on Social Networking Sites: The Role of Perceived Control of Information (2014, background): “A more recent study conducted by Liu et al. (2011) measures the disparity between the desired and actual privacy settings and shows that privacy setting match users’ expectations only 37 % of the time, quantifying the magnitude of the problem of managing privacy on Facebook.”
- Privacy-CNH: A Framework to Detect Photo Privacy with Convolutional Neural Network using Hierarchical Features (2016, methodology): “Similar to the work by (Zerr et al. 2012), the work by (Liu et al. 2011) also developed an user interface to use workers on Amazon Mechanical Turk (AMT) to label their dataset.”