Secure Multiparty PageRank Algorithm for Collaborative Fraud Detection

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

12 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationFinancial Cryptography and Data Security
Subtitle of host publication23rd International Conference, FC 2019, Frigate Bay, St. Kitts and Nevis, February 18–22, 2019, Revised Selected Papers
EditorsIan Goldberg, Tyler Moore
Place of PublicationCham
PublisherSpringer Nature
Pages605-623
Number of pages19
ISBN (Electronic)978-3-030-32101-7
ISBN (Print)978-3-030-32100-0
DOIs
Publication statusPublished - 2019
Externally publishedYes
Event23rd International Conference on Financial Cryptography and Data Security, FC 2019 - St. Kitts, Saint Kitts and Nevis
Duration: 18 Feb 201922 Feb 2019
Conference number: 23

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11598
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference23rd International Conference on Financial Cryptography and Data Security, FC 2019
Abbreviated titleFC 2019
Country/TerritorySaint Kitts and Nevis
CitySt. Kitts
Period18/02/1922/02/19

Keywords

  • Collaborative computation
  • Fraud detection
  • Multiparty computation
  • PageRank
  • n/a OA procedure

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