Enabling Data Markets Using Smart Contracts and Multi-party Computation

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)


With the emergence of data markets, data have become an asset that is used as part of transactions. Current data markets rely on trusted third parties to manage the data, creating single points of failure with possibly disastrous consequences on data privacy and security. The lack of technical solutions to enforce strong privacy and security guarantees leaves the data markets’ stakeholders (e.g., buyers and sellers of data) vulnerable when they transact data. Smart Contracts and Multi-Party Computation represent examples of emerging technologies that have the potential to guarantee the desired levels of data privacy and security. In this paper, we propose an architecture for data markets based on Smart Contracts and Multi-Party Computation and present a proof of concept prototype developed to demonstrate the feasibility of the proposed architecture.


Multi-party computation Smart contracts Data markets 



This work is partly funded by the EC H2020 projects euBusinessGraph (Grant number: 732003), EW-Shopp (Grant number: 732590), and TheyBuyForYou (Grant number: 780247). We thank the Sharemind team for promptly answering questions regarding Sharemind.


  1. 1.
  2. 2.
    Bogdanov, D.: Sharemind: programmable secure computations with practical applications, Doctoral dissertation (2013)Google Scholar
  3. 3.
    Jagomägis, R.: Secrec: a privacy-aware programming language with applications in data mining. Master’s thesis, University of Tartu (2010)Google Scholar
  4. 4.
    Datum White Paper v15 (2017).
  5. 5.
    Zyskind, G., Nathan, O., Pentland, A.: Enigma: decentralized computation platform with guaranteed privacy. arXiv preprint arXiv:1506.03471 (2015)
  6. 6.
    Insights Network - A Blockchain Data Exchange White Paper (2017).
  7. 7.
    Roman, D., Stefano, G.: Towards a reference architecture for trusted data marketplaces: the credit scoring perspective. In: International Conference on Open and Big Data (OBD). IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.SINTEF ASOsloNorway

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