DBScholar

Back to papers

K-Isomorphism: Privacy Preserving Network Publication against Structural Attacks

Summary: Proposes k-isomorphism as the necessary-and-sufficient privacy model against structural attacks on graphs (NodeInfo/LinkInfo). NP-hard; develops efficiency techniques and a compound vertex ID for multi-release privacy; validated on HEP-TH, EUemail, LiveJournal, showing symmetry aids anonymization while preserving utility. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4328
Venue
SIGMOD
Year
2010
Pagerank
6.3085805e-05
Overall Rank
5,227 | 64.14%
DOI
10.1145/1807167.1807218

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{cheng_sigmod10,
        title = {{K-Isomorphism: Privacy Preserving Network Publication against Structural Attacks}},
        author = {Cheng, James and Fu, Ada Wai-Chee and Liu, Jia},
        series = {{SIGMOD} '10},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1807167.1807218},
        url = {https://dl.acm.org/doi/10.1145/1807167.1807218},
        year = {2010}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers