On enabling machine learning tasks atop public blockchains: A crowdsourcing approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The recently emerged blockchain (in particular smart contract) technology offers an enticing opportunity for decentralized sharing economy. Machine learning can be one important subroutine in such a decentralized ecosystem. Unfortunately, machine learning programs are usually computational intensive as well as randomized, which fall into the inherent limitations of open blockchain where complex and randomized programs cannot be executed by the underlying nodes collectively. Given also the limitations of existing verifiable computing techniques, we propose a crowdsourcing idea from the game theoretic perspective to resolve the tension. We design a simple incentive mechanism so that the execution of a wide range of complex programs can be crowdsourced via the blockchain, and any false computing result could be deterred. In particular, our protocol works in the scenarios that there is no trusted third-party involved; Moreover, our protocol not only works in the classical model of non-colluding service providers, but also can tolerate any potential coalition up to n-1, where n is the total number of service providers. We also showcase how to use our protocol to crowdsource two typical kinds of machine learning tasks via open blockchain. We envision that our solution is not only promising to launch decentralized applications involving a wide range of machine learning programs, but also a stepping stone towards a general way to empowering intensive and randomized computations atop the open blockchain.

Original languageEnglish (US)
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsFeida Zhu, Zhenhui Li, Jeffrey Yu, Hanghang Tong
PublisherIEEE Computer Society
Pages81-88
Number of pages8
ISBN (Electronic)9781538692882
DOIs
StatePublished - Feb 7 2019
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
Country/TerritorySingapore
CitySingapore
Period11/17/1811/20/18

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

Keywords

  • blockchain
  • crowdsourcing
  • game theory
  • machine learning
  • smart contract

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