Countermeasures Against False-Name Attacks on Truthful Incentive Mechanisms for Crowdsourcing

Abstract

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.