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Scalable non-deterministic clustering-based k-anonymization for rich networks

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Abstract

In this paper, we tackle the problem of graph anonymization in the context of privacy-preserving social network mining. We present a greedy and non-deterministic algorithm to achieve k-anonymity on labeled and undirected networks. Our work aims to create a scalable algorithm for real-world big networks, which runs in parallel and uses biased randomization for improving the quality of the solutions. We propose new metrics that consider the utility of the clusters from a recommender system point of view. We compare our approach to SaNGreeA, a well-known state-of-the-art algorithm for k-anonymity generalization. Finally, we have performed scalability tests, with up to 160 machines within the Hadoop framework, for anonymizing a real-world dataset with around 830 K nodes and 63 M relationships, demonstrating our method’s utility and practical applicability.

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Notes

  1. http://www.xing.com.

  2. The Scala source code is available at: https://bitbucket.org/jcasasr/scan-algorithm/.

  3. Available at: http://igraph.org.

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Acknowledgements

Jordi Casas-Roma was partially supported by the Spanish MCYT and the FEDER funds under Grants TIN2011-27076-C03 “CO-PRIVACY” and TIN2014-57364-C2-2-R “SMARTGLACIS.” Julián Salas acknowledges the support of a UOC postdoctoral fellowship.

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Authors and Affiliations

  1. XING, Barcelona, Spain

    Miguel Ros-Martín

  2. Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), Barcelona, Spain

    Julián Salas & Jordi Casas-Roma

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  1. Miguel Ros-Martín
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  2. Julián Salas
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  3. Jordi Casas-Roma
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Correspondence to Jordi Casas-Roma.

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Ros-Martín, M., Salas, J. & Casas-Roma, J. Scalable non-deterministic clustering-based k-anonymization for rich networks. Int. J. Inf. Secur. 18, 219–238 (2019). https://doi.org/10.1007/s10207-018-0409-1

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