Over-exposed?: privacy patterns and considerations in online and mobile photo sharing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Characterizing privacy in online social networks
Proceedings of the first workshop on Online social networks
Risk Evaluation for Personal Identity Management Based on Privacy Attribute Ontology
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Collective privacy management in social networks
Proceedings of the 18th international conference on World wide web
All your contacts are belong to us: automated identity theft attacks on social networks
Proceedings of the 18th international conference on World wide web
FaceCloak: An Architecture for User Privacy on Social Networking Sites
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
A Framework for Computing the Privacy Scores of Users in Online Social Networks
ICDM '09 Proceedings of the 2009 Ninth IEEE International Conference on Data Mining
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
A3P: adaptive policy prediction for shared images over popular content sharing sites
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Imagined communities: awareness, information sharing, and privacy on the facebook
PET'06 Proceedings of the 6th international conference on Privacy Enhancing Technologies
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Social networking services (SNSs) are regarded as an indispensable social media for finding friends and interacting with them. However, their search capabilities often raise privacy concerns. Usually, an SNS provides privacy settings for each user, so that he/she can specify who can access his/her online contents. But these privacy settings often become either too simplistic or too complicated. To assist SNS users to discover their own appropriate settings, we propose a privacy-setting recommendation system, which utilizes privacy settings on public access, collected from over 66,000 real Facebook users and settings donated by participating users. We show privacy scores of the collected settings according to user categories. Our recommendation system utilizes these analysis results as well as correlations within privacy settings, and visualizes distribution of collected user's settings. Our evaluations on test users show effectiveness of our approach.