IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v69y2023i9p5482-5500.html
   My bibliography  Save this article

Privacy-Preserving Network Analytics

Author

Listed:
  • Marcella Hastings

    (Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104; Bolt Labs, Inc, Baltimore, Maryland 21215)

  • Brett Hemenway Falk

    (Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

  • Gerry Tsoukalas

    (Questrom School of Business, Boston University, Boston, Massachusetts 02215)

Abstract

We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between established theories of financial network contagion and systemic risk that assume agents have full network information and the real world where information sharing is hindered by privacy and security concerns.

Suggested Citation

  • Marcella Hastings & Brett Hemenway Falk & Gerry Tsoukalas, 2023. "Privacy-Preserving Network Analytics," Management Science, INFORMS, vol. 69(9), pages 5482-5500, September.
    • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5482-5500
    DOI: 10.1287/mnsc.2022.4582
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2022.4582
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.2022.4582?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:69:y:2023:i:9:p:5482-5500. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.