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Cited by 549

The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.

Resisting structural re-identification in anonymized social networks

Open access
Hay, Michael, Miklau, Gerome, Jensen, David, Towsley, Don, Weis, Philipp·2008·Proceedings of the VLDB Endowment

Abstract

We identify privacy risks associated with releasing network data sets and provide an algorithm that mitigates those risks. A network consists of entities connected by links representing relations such as friendship, communication, or shared activity. Maintaining privacy when publishing networked data is uniquely challenging because an individual's network context can be used to identify them even if other identifying information is removed. In this paper, we quantify the privacy risks associated with three classes of attacks on the privacy of individuals in networks, based on the knowledge used by the adversary. We show that the risks of these attacks vary greatly based on network structure and size. We propose a novel approach to anonymizing network data that models aggregate network structure and then allows samples to be drawn from that model. The approach guarantees anonymity for network entities while preserving the ability to estimate a wide variety of network measures with relatively little bias.

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Metadata

Authors
Hay, M., Miklau, G., Jensen, D., Towsley, D., & Weis, P.
Venue
Proceedings of the VLDB Endowment
Published
2008-08
Volume
1
Issue
1
Pages
102-114
ISSN
2150-8097 (ISSN-L)
OpenAlex
W2124849257
Cited by
549
References
57
FWCI
60.95
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Resisting structural re-identification in anonymized social networks. https://app.cochat.ai/papers/10.14778/1453856.1453873
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Hay, M., Miklau, G., Jensen, D., Towsley, D., & Weis, P. (2008). Resisting structural re-identification in anonymized social networks. Proceedings of the VLDB Endowment. https://doi.org/10.14778/1453856.1453873

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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.