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An Evaluation of Edge Modification Techniques for Privacy-Preserving on Graphs

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Modeling Decisions for Artificial Intelligence (MDAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9321))

Abstract

Noise is added by privacy-preserving methods or anonymization processes to prevent adversaries from re-identifying users in anonymous networks. The noise introduced by the anonymization steps may also affect the data, reducing its utility for subsequent data mining processes. Graph modification approaches are one of the most used and well-known methods to protect the privacy of the data. These methods converts the data by edges or vertices modifications before releasing the perturbed data. In this paper we want to analyse the edge modification techniques found in the literature covering this topic, and then empirically evaluate the information loss introduced by each of these methods. We want to point out how these methods affect the main properties and characteristics of the network, since it will help us to choose the best one to achieve a desired privacy level while preserving data utility.

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Notes

  1. 1.

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

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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. Universitat Oberta de Catalunya, Barcelona, Spain

    Jordi Casas-Roma

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  1. Jordi Casas-Roma
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Correspondence to Jordi Casas-Roma .

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Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Vicenc Torra

  2. Toho Gakuen, Tokyo, Japan

    Torra Narukawa

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Casas-Roma, J. (2015). An Evaluation of Edge Modification Techniques for Privacy-Preserving on Graphs. In: Torra, V., Narukawa, T. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2015. Lecture Notes in Computer Science(), vol 9321. Springer, Cham. https://doi.org/10.1007/978-3-319-23240-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-23240-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23239-3

  • Online ISBN: 978-3-319-23240-9

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