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Community-preserving anonymization of graphs

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Abstract

In this paper, we propose a novel edge modification technique that better preserves the communities of a graph while anonymizing it. By maintaining the core number sequence of a graph, its coreness, we retain most of the information contained in the network while allowing changes in the degree sequence, i. e. obfuscating the visible data an attacker has access to. We reach a better trade-off between data privacy and data utility than with existing methods by capitalizing on the slack between apparent degree (node degree) and true degree (node core number). Our extensive experiments on six diverse standard network datasets support this claim. Our framework compares our method to other that are used as proxies for privacy protection in the relevant literature. We demonstrate that our method leads to higher data utility preservation, especially in clustering, for the same levels of randomization and k-anonymity.

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Notes

  1. A preliminary version of this work appeared in the PhD thesis of one of the authors [10].

  2. In cell biology, the nuclear lamina is a dense fibrillar network that surrounds the nucleus, gives it its shape and stabilizes the nuclear membrane.

  3. http://igraph.org/python/doc/igraph.GraphBase-class.html.

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Acknowledgements

This work was partly funded by the Spanish MCYT and the FEDER funds under Grants TIN2011-27076-C03 “CO-PRIVACY” and TIN2014-57364-C2-2-R “SMARTGLACIS”.

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Authors and Affiliations

  1. LIX, École Polytechnique, Palaiseau, France

    François Rousseau & Michalis Vazirgiannis

  2. Universitat Oberta de Catalunya (UOC), Barcelona, Spain

    Jordi Casas-Roma

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  1. François Rousseau
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  2. Jordi Casas-Roma
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  3. Michalis Vazirgiannis
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Correspondence to Jordi Casas-Roma.

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Rousseau, F., Casas-Roma, J. & Vazirgiannis, M. Community-preserving anonymization of graphs. Knowl Inf Syst 54, 315–343 (2018). https://doi.org/10.1007/s10115-017-1064-y

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