Privacy-Preserving Collaborative Social Networks
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
k-symmetry model for identity anonymization in social networks
Proceedings of the 13th International Conference on Extending Database Technology
Online anonymity protection in computer-mediated communication
IEEE Transactions on Information Forensics and Security
Resisting structural re-identification in anonymized social networks
The VLDB Journal — The International Journal on Very Large Data Bases
Secure collaborative social networks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Stalking online: on user privacy in social networks
Proceedings of the second ACM conference on Data and Application Security and Privacy
Anonymity and roles associated with aggressive posts in an online forum
Computers in Human Behavior
EWNI: efficient anonymization of vulnerable individuals in social networks
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
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While privacy preservation of data mining approaches has been an important topic for a number of years, privacy of social network data is a relatively new area of interest. Previous research has shown that anonymization alone may not be sufficient for hiding identity information on certain real world data sets. In this paper, we focus on understand- ing the impact of network topology and node substructure on the level of anonymity present in the network. We present a new measure, topological anonymity, that quantifies the amount of privacy preserved in different topological struc- tures. The measure uses a combination of known social net- work metrics and attempts to identify when node and edge inference breeches arise in these graphs.