MPC Joins The Dark Side

John Cartlidge, Nigel Smart, Younes Talibi Alaoui

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
320 Downloads (Pure)

Abstract

We consider the issue of securing dark pools/markets in the financial services sector. These markets currently are executed via trusted third parties, leading to potential fraud being able to be conducted by the market operators. We present a potential solution to this problem by using Multi-Party Computation to enable a trusted third party to be emulated in software. Our experiments show that whilst the standard market clearing mechanism of Continuous Double Auction in lit markets is not currently viable when executed using MPC, a popular mechanism for evaluating dark markets, namely the volume matching methodology, is viable. We present experimental validation of this conclusion by presenting the expected throughputs for such markets in two popular MPC paradigms; namely the two party dishonest majority setting and the honest majority three party setting.
Original languageEnglish
Title of host publicationAsiaCCS 2019 - Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery (ACM)
Pages148-159
Number of pages12
ISBN (Print)9781450367523
DOIs
Publication statusPublished - 12 Jul 2019
Event2019 ACM Asia Conference on Computer and Communications Security, AsiaCCS 2019 - Auckland, New Zealand
Duration: 9 Jul 201912 Jul 2019

Conference

Conference2019 ACM Asia Conference on Computer and Communications Security, AsiaCCS 2019
CountryNew Zealand
CityAuckland
Period9/07/1912/07/19

Structured keywords

  • Cyber Security

Keywords

  • cryptographic protocols
  • financial markets
  • Financial Engineering
  • AUCTION DESIGN

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  • Cite this

    Cartlidge, J., Smart, N., & Talibi Alaoui, Y. (2019). MPC Joins The Dark Side. In AsiaCCS 2019 - Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security (pp. 148-159). Association for Computing Machinery (ACM). https://doi.org/10.1145/3321705.3329809