k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Analysis of topological characteristics of huge online social networking services
Proceedings of the 16th international conference on World Wide Web
Challenges in mining social network data: processes, privacy, and paradoxes
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preserving Privacy in Social Networks Against Neighborhood Attacks
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Measuring risk and utility of anonymized data using information theory
Proceedings of the 2009 EDBT/ICDT Workshops
A Practical Attack to De-anonymize Social Network Users
SP '10 Proceedings of the 2010 IEEE Symposium on Security and Privacy
On the protection of social networks user's information
Knowledge-Based Systems
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We consider two distinct types of online social network, the first made up of a log of writes to wall by users in Facebook, and the second consisting of a corpus of emails sent and received in a corporate environment (Enron). We calculate the statistics which describe the topologies of each network represented as a graph. Then we calculate the information loss and risk of disclosure for different percentages of perturbation for each dataset, where perturbation is achieved by randomly adding links to the nodes. We find that the general tendency of information loss is similar, although Facebook is affected to a greater extent. For risk of disclosure, both datasets also follow a similar trend, except for the average path length statistic. We find that the differences are due to the different distributions of the derived factors, and also the type of perturbation used and its parameterization. These results can be useful for choosing and tuning anonymization methods for different graph datasets.