The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Towards identity anonymization on graphs
Abstract
The proliferation of network data in various application domains has raised privacy concerns for the individuals involved. Recent studies show that simply removing the identities of the nodes before publishing the graph/social network data does not guarantee privacy. The structure of the graph itself, and in its basic form the degree of the nodes, can be revealing the identities of individuals. To address this issue, we study a specific graph-anonymization problem. We call a graph k-degree anonymous if for every node v, there exist at least k-1 other nodes in the graph with the same degree as v. This definition of anonymity prevents the re-identification of individuals by adversaries with a priori knowledge of the degree of certain nodes. We formally define the graph-anonymization problem that, given a graph G, asks for the k-degree anonymous graph that stems from G with the minimum number of graph-modification operations. We devise simple and efficient algorithms for solving this problem. Our algorithms are based on principles related to the realizability of degree sequences. We apply our methods to a large spectrum of synthetic and real datasets and demonstrate their efficiency and practical utility.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Rayward-Smith, V. J. (1991) · Journal of the Operational Research Society · doi:10.1057/jors.1991.155 · Cited by 16,821
L-diversity: privacy beyond k-anonymity
Machanavajjhala, A., Gehrke, J., Kifer, D., & Venkitasubramaniam, M. (2006) · 22nd International Conference on Data Engineering (ICDE'06) · doi:10.1109/icde.2006.1 · Cited by 2,362
Networks, Dynamics, and the Small‐World Phenomenon
Watts, D. J. (1999) · American Journal of Sociology · doi:10.1086/210318 · Cited by 1,510
Data Privacy through Optimal k-Anonymization
Bayardo, R. J., & Agrawal, R. (2005) · 21st International Conference on Data Engineering (ICDE'05) · doi:10.1109/icde.2005.42 · Cited by 1,131
Getoor, L., & Diehl, C. P. (2005) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1117454.1117456 · Cited by 973
Backstrom, L., Dwork, C., & Kleinberg, J. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242598 · Cited by 850
On the complexity of optimal K-anonymity
Meyerson, A., & Williams, R. (2004) · Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/1055558.1055591 · Cited by 821
Generalizing data to provide anonymity when disclosing information (abstract)
Samarati, P., & Sweeney, L. (1998) · Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems · doi:10.1145/275487.275508 · Cited by 779
Cited by
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
“Ways of anonymizing such data have been considered in the literature [37, 220].”
BackgroundA Review of Anonymization for Healthcare Data
Olatunji, I. E., Rauch, J., Katzensteiner, M., & Khosla, M. (2024) · Big Data · doi:10.1089/big.2021.0169 · Cited by 101
Improved l-diversity: scalable anonymization approach for privacy preserving big data publishing
Mehta, B. B., & Rao, U. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.08.006 · Cited by 57
Metadata
- Authors
- Liu, K., & Terzi, E.
- Venue
- Proceedings of the 2008 ACM SIGMOD international conference on Management of data
- Publisher
- ACM
- Published
- 2008-06-09
- Pages
- 93-106
- ISSN
- 0730-8078 (ISSN-L)
- OpenAlex
- W1998091733
- Cited by
- 773
- References
- 18
- FWCI
- 33.99
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Towards identity anonymization on graphs. https://app.cochat.ai/papers/10.1145/1376616.1376629
- The paper
- Liu, K., & Terzi, E. (2008). Towards identity anonymization on graphs. Proceedings of the 2008 ACM SIGMOD international conference on Management of data. https://doi.org/10.1145/1376616.1376629
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
