Countermeasures against false-name attacks on truthful incentive mechanisms for crowdsourcing

Xiang Zhang, Guoliang Xue, Ruozhou Yu, Dejun Yang, Jian Tang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


The proliferation of crowdsourcing brings both opportunities and challenges in various fields, such as environmental monitoring, healthcare, and so on. Often, the collaborative efforts from a large crowd of users are needed in order to complete crowdsourcing jobs. In recent years, the design of crowdsourcing incentive mechanisms has drawn much interest from the research community, where auction is one of the commonly adopted mechanisms. However, few of these auctions consider the robustness against false-name attacks (a.k.a. sybil attacks), where dishonest users generate fake identities to increase their utilities without devoting more efforts. To provide countermeasures against such attacks, we have designed a Truthful Auction with countermeasures against False-name Attacks (TAFA) as an auction-based incentive mechanism for crowdsourcing. We prove that TAFA is truthful, individually rational, budget-balanced, and computationally efficient. We also prove that TAFA provides countermeasures against false-name attacks, such that each user is better off not generating any false name. Extensive performance evaluations are conducted and the results further confirm our theoretical analysis.

Original languageEnglish (US)
Article number7835128
Pages (from-to)478-485
Number of pages8
JournalIEEE Journal on Selected Areas in Communications
Issue number2
StatePublished - Feb 2017


  • Game theory
  • crowdsourcing
  • false-name proofness
  • incentive mechanism
  • truthfulness

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering


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