Secure Multiparty PageRank Algorithm for Collaborative Fraud Detection

  • Alex SangersEmail author
  • Maran van Heesch
  • Thomas Attema
  • Thijs Veugen
  • Mark Wiggerman
  • Jan Veldsink
  • Oscar Bloemen
  • Daniël Worm
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11598)


Collaboration between financial institutions helps to improve detection of fraud. However, exchange of relevant data between these institutions is often not possible due to privacy constraints and data confidentiality. An important example of relevant data for fraud detection is given by a transaction graph, where the nodes represent bank accounts and the links consist of the transactions between these accounts. Previous works show that features derived from such graphs, like PageRank, can be used to improve fraud detection. However, each institution can only see a part of the whole transaction graph, corresponding to the accounts of its own customers. In this research a new method is described, making use of secure multiparty computation (MPC) techniques, allowing multiple parties to jointly compute the PageRank values of their combined transaction graphs securely, while guaranteeing that each party only learns the PageRank values of its own accounts and nothing about the other transaction graphs. In our experiments this method is applied to graphs containing up to tens of thousands of nodes. The execution time scales linearly with the number of nodes, and the method is highly parallelizable. Secure multiparty PageRank is feasible in a realistic setting with millions of nodes per party by extrapolating the results from our experiments.


Multiparty computation PageRank Fraud detection Collaborative computation 



The research activities that have led to this paper were funded by the Shared Research Program Cyber Security; a research collaboration between TNO, ABN AMRO, Rabobank, ING, Achmea and Volksbank. The authors would also like to thank Gabriele Spini for his valuable feedback and his help in improving the paper.


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Copyright information

© International Financial Cryptography Association 2019

Authors and Affiliations

  • Alex Sangers
    • 1
    Email author
  • Maran van Heesch
    • 1
  • Thomas Attema
    • 1
    • 5
  • Thijs Veugen
    • 1
    • 5
  • Mark Wiggerman
    • 2
  • Jan Veldsink
    • 3
  • Oscar Bloemen
    • 4
  • Daniël Worm
    • 1
  1. 1.Netherlands Organisation for Applied Scientific Research (TNO)The HagueThe Netherlands
  2. 2.ABN AMROAmsterdamThe Netherlands
  3. 3.RabobankUtrechtThe Netherlands
  4. 4.INGAmsterdamThe Netherlands
  5. 5.CWIAmsterdamThe Netherlands

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