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Smart Contracts for Incentivized Outsourcing of Computation

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Data Privacy Management, Cryptocurrencies and Blockchain Technology (DPM 2021, CBT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13140))

Abstract

Outsourcing computation allows a resource limited client to expand its computational capabilities by outsourcing computation to other computing nodes or clouds. A basic requirement of outsourcing is providing assurance that the computation result is correct. We consider a smart contract based outsourcing system that achieves assurance by replicating the computation on two servers, and accepts the computation result if the two responses match. Correct computation result is obtained by using incentivization to instigate correct behaviour in servers. We show that all previous replication based incentivized outsourcing protocols with proven correctness fail when automated by a smart contract, because of the copy attack where a contractor simply copies the submitted response of the other contractor. We then design an incentivization mechanism that uses two lightweight challenge-response protocols that are used when the submitted results are compared, and employs monetary rewards, fines, and bounties to incentivize correct computation. We use game theory to model and analyze our mechanism, and prove that with appropriate choices of the mechanism parameters, there is a single Nash equilibrium corresponding to the contractors’ strategy of correctly computing the result. Our work provides a foundation for replicated incentivized computation in smart contract setting, and opens new research directions.

Full version available on IACR ePrint Archive, Report 2021/174.

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Notes

  1. 1.

    The same assumption as [4, 17].

  2. 2.

    In fact, if the client was able to know one of the clouds is honest, then with high likelihood can determine which of the two is the trustworthy one.

  3. 3.

    A randomized algorithm can be outsourced after de-randomization using a pseudorandom generator.

  4. 4.

    Recall that the difference between G and L strategies is that the response submitted by two L contractors will match, whereas the response submitted by two G contractors will not match, except with negligible probability. Thus, the Lazy contractor paradigm is enough to model submitting matching guesses (e.g., using the same pseudorandom seed).

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Acknowledgements

Alptekin Küpçü acknowledges support from TÜBİTAK, the Scientific and Technological Research Council of Turkey, under project number 119E088. The work of Reihaneh Safavi-Naini has been in part supported by Natural Sciences and Engineering Research Council of Canada Discovery Grant Program.

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Authors and Affiliations

  1. Koç University, İstanbul, Turkey

    Alptekin Küpçü

  2. University of Calgary, Calgary, Alberta, Canada

    Reihaneh Safavi-Naini

Authors
  1. Alptekin Küpçü
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  2. Reihaneh Safavi-Naini
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Correspondence to Alptekin Küpçü .

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Editors and Affiliations

  1. Institut Polytechnique de Paris, Palaiseau, France

    Joaquin Garcia-Alfaro

  2. Universitat Politècnica de Catalunya, Barcelona, Spain

    Jose Luis Muñoz-Tapia

  3. Universitat Autonoma de Barcelona, Bellaterra, Spain

    Guillermo Navarro-Arribas

  4. Universitat Politècnica de Catalunya, Barcelona, Spain

    Miguel Soriano

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Küpçü, A., Safavi-Naini, R. (2022). Smart Contracts for Incentivized Outsourcing of Computation. In: Garcia-Alfaro, J., Muñoz-Tapia, J.L., Navarro-Arribas, G., Soriano, M. (eds) Data Privacy Management, Cryptocurrencies and Blockchain Technology. DPM CBT 2021 2021. Lecture Notes in Computer Science(), vol 13140. Springer, Cham. https://doi.org/10.1007/978-3-030-93944-1_16

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  • DOI: https://doi.org/10.1007/978-3-030-93944-1_16

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