Towards Identity Anonymization on Graphs
Summary: Defines k-degree anonymity: every node has at least k−1 others with the same degree, enabling privacy against degree-based re-identification. Presents minimal-graph-modification algorithms to reach a closest k-degree anonymous graph, leveraging degree-sequence realizability; validated on synthetic and real networks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kun Liu (IBM)
- 2. Evimaria Terzi (IBM)
BibTeX Citation
@inproceedings{liu_sigmod08,
title = {{Towards Identity Anonymization on Graphs}},
author = {Liu, Kun and Terzi, Evimaria},
series = {{SIGMOD} '08},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1376616.1376629},
url = {https://dl.acm.org/doi/10.1145/1376616.1376629},
year = {2008}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 338 | Generalizing Data to Provide Anonymity when Disclosing Information | 1998 | PODS | 0.00020731054 |
| 384 | On the Complexity of Optimal K-Anonymity | 2004 | PODS | 0.00019510305 |
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