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SIGMOD
2010
ACM

K-isomorphism: privacy preserving network publication against structural attacks

10 years 2 months ago
K-isomorphism: privacy preserving network publication against structural attacks
Serious concerns on privacy protection in social networks have been raised in recent years; however, research in this area is still in its infancy. The problem is challenging due to the diversity and complexity of graph data, on which an adversary can use many types of background knowledge to conduct an attack. One popular type of attacks as studied by pioneer work [2] is the use of embedding subgraphs. We follow this line of work and identify two realistic targets of attacks, namely, NodeInfo and LinkInfo. Our investigations show that k-isomorphism, or anonymization by forming k pairwise isomorphic subgraphs, is both sufficient and necessary for the protection. The problem is shown to be NP-hard. We devise a number of techniques to enhance the anonymization efficiency while retaining the data utility. The satisfactory performance on a number of real datasets, including HEP-Th, EUemail and LiveJournal, illustrates that the high symmetry of social networks is very helpful in mitigati...
James Cheng, Ada Wai-Chee Fu, Jia Liu
Added 18 Jul 2010
Updated 18 Jul 2010
Type Conference
Year 2010
Where SIGMOD
Authors James Cheng, Ada Wai-Chee Fu, Jia Liu
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