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Privacy-Preserving Link Prediction

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Data Privacy Management, Cryptocurrencies and Blockchain Technology (DPM 2022, CBT 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13619))

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Abstract

Consider two data holders, ABC and XYZ, with graph data (e.g., social networks, e-commerce, telecommunication, and bio-informatics). ABC can see that node A is linked to node B, and XYZ can see node B is linked to node C. Node B is the common neighbour of A and C but neither network can discover this fact on their own. In this paper, we provide a two party computation that ABC and XYZ can run to discover the common neighbours in the union of their graph data, however neither party has to reveal their plaintext graph to the other. Based on private set intersection, we implement our solution, provide measurements, and quantify partial leaks of privacy. We also propose a heavyweight solution that leaks zero information based on additively homomorphic encryption.

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Notes

  1. 1.

    Full paper.

  2. 2.

    NIST Special Publication 800-131A Revision 2.

  3. 3.

    GitHub: SALab.

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Acknowledgements

We thank the reviewers who helped to improve our paper. J. Clark acknowledges support for this research project from the National Sciences and Engineering Research Council (NSERC), Raymond Chabot Grant Thornton, and Catallaxy Industrial Research Chair in Blockchain Technologies and NSERC through a Discovery Grant. E. Ayday acknowledges that research reported in this paper was partly supported by the National Science Foundation (NSF) under grant number NSF CCF 2200255 and Cisco Research University Funding grant number 2800379. M. Namazi acknowledges that this work was partially funded by the Spanish Government through grant RTI2018-095094-B-C22.

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Authors and Affiliations

  1. Concordia University, Montreal, QC, Canada

    Didem Demirag & Jeremy Clark

  2. Open University of Catalonia, Barcelona, Spain

    Mina Namazi

  3. Case Western Reserve University, Cleveland, OH, USA

    Erman Ayday

Authors
  1. Didem Demirag
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  2. Mina Namazi
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  3. Erman Ayday
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  4. Jeremy Clark
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Corresponding author

Correspondence to Didem Demirag .

Editor information

Editors and Affiliations

  1. Samovar, Télécom SudParis, Institut Polytechnique de Paris, Palaiseau, France

    Joaquin Garcia-Alfaro

  2. Universitat Autonoma de Barcelona, Bellaterra, Spain

    Guillermo Navarro-Arribas

  3. Technical University of Denmark, Lyngby, Denmark

    Nicola Dragoni

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Demirag, D., Namazi, M., Ayday, E., Clark, J. (2023). Privacy-Preserving Link Prediction. In: Garcia-Alfaro, J., Navarro-Arribas, G., Dragoni, N. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2022 2022. Lecture Notes in Computer Science, vol 13619. Springer, Cham. https://doi.org/10.1007/978-3-031-25734-6_3

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  • DOI: https://doi.org/10.1007/978-3-031-25734-6_3

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  • Print ISBN: 978-3-031-25733-9

  • Online ISBN: 978-3-031-25734-6

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