Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
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
Anonymizing bipartite graph data using safe groupings
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
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
K-isomorphism: privacy preserving network publication against structural attacks
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Identity obfuscation in graphs through the information theoretic lens
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Discretionary social network data revelation with a user-centric utility guarantee
Proceedings of the 21st ACM international conference on Information and knowledge management
How to hack into Facebook without being a hacker
Proceedings of the 22nd international conference on World Wide Web companion
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Social network data analysis raises concerns about the privacy of related entities or individuals. To address this issue, organizations can publish data after simply replacing the identities of individuals with pseudonyms, leaving the overall structure of the social network unchanged. However, it has been shown that attacks based on structural identification (e.g., a walk-based attack) enable an adversary to re-identify selected individuals in an anonymized network. In this paper we explore the capacity of techniques based on random edge perturbation to thwart such attacks. We theoretically establish that any kind of structural identification attack can effectively be prevented using random edge perturbation and show that, surprisingly, important properties of the whole network, as well as of subgraphs thereof, can be accurately calculated and hence data analysis tasks performed on the perturbed data, given that the legitimate data recipient knows the perturbation probability as well. Yet we also examine ways to enhance the walk-based attack, proposing a variant we call probabilistic attack. Nevertheless, we demonstrate that such probabilistic attacks can also be prevented under sufficient perturbation. Eventually, we conduct a thorough theoretical study of the probability of success of any}structural attack as a function of the perturbation probability. Our analysis provides a powerful tool for delineating the identification risk of perturbed social network data; our extensive experiments with synthetic and real datasets confirm our expectations.