Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Information revelation and privacy in online social networks
Proceedings of the 2005 ACM workshop on Privacy in the electronic society
Proceedings of the 16th international conference on World Wide Web
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Detecting and resolving privacy conflicts for collaborative data sharing in online social networks
Proceedings of the 27th Annual Computer Security Applications Conference
Preemptive mechanism to prevent health data privacy leakage
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
FORPS: friends-oriented reputation privacy score
Proceedings of the First International Workshop on Security and Privacy Preserving in e-Societies
Stalking online: on user privacy in social networks
Proceedings of the second ACM conference on Data and Application Security and Privacy
On the complexity of aggregating information for authentication and profiling
DPM'11 Proceedings of the 6th international conference, and 4th international conference on Data Privacy Management and Autonomous Spontaneus Security
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A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities. In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.