Privacy wizards for social networking sites
Proceedings of the 19th international conference on World wide web
Coprivacy: towards a theory of sustainable privacy
PSD'10 Proceedings of the 2010 international conference on Privacy in statistical databases
Rational privacy disclosure in social networks
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Privacy-aware data management in information networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A3P: adaptive policy prediction for shared images over popular content sharing sites
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Trend analysis and recommendation of users' privacy settings on social networking services
SocInfo'11 Proceedings of the Third international conference on Social informatics
ROAuth: recommendation based open authorization
Proceedings of the Seventh Symposium on Usable Privacy and Security
Indirect content privacy surveys: measuring privacy without asking about it
Proceedings of the Seventh Symposium on Usable Privacy and Security
Stalking online: on user privacy in social networks
Proceedings of the second ACM conference on Data and Application Security and Privacy
Fine-grained access control of personal data
Proceedings of the 17th ACM symposium on Access Control Models and Technologies
The PViz comprehension tool for social network privacy settings
Proceedings of the Eighth Symposium on Usable Privacy and Security
Privacy-Preserving EM algorithm for clustering on social network
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
What's in a name?: an unsupervised approach to link users across communities
Proceedings of the sixth ACM international conference on Web search and data mining
| Hi-index | 0.00 |
A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. The main focus was on how to share social networks owned by organizations without revealing the identities or sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their information sharing activities. In this paper, we approach the privacy concerns arising in online social networks from the individual users’ viewpoint: we propose a framework to compute a privacy score of a user, which indicates the potential privacy risk caused by his participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive the information revealed by a user, the higher his 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.