Paper 2018/917

Secure multiparty PageRank algorithm for collaborative fraud detection

Alex Sangers, Maran van Heesch, Thomas Attema, Thijs Veugen, Mark Wiggerman, Jan Veldsink, Oscar Bloemen, and Daniël Worm


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.

Available format(s)
Cryptographic protocols
Publication info
Preprint. MINOR revision.
Multiparty computationPageRankfraud detectioncollaborative computation
Contact author(s)
alex sangers @ tno nl
2018-10-02: received
Short URL
Creative Commons Attribution


      author = {Alex Sangers and Maran van Heesch and Thomas Attema and Thijs Veugen and Mark Wiggerman and Jan Veldsink and Oscar Bloemen and Daniël Worm},
      title = {Secure multiparty PageRank algorithm for collaborative fraud detection},
      howpublished = {Cryptology ePrint Archive, Paper 2018/917},
      year = {2018},
      note = {\url{}},
      url = {}
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