TY - JOUR
T1 - Countermeasures against false-name attacks on truthful incentive mechanisms for crowdsourcing
AU - Zhang, Xiang
AU - Xue, Guoliang
AU - Yu, Ruozhou
AU - Yang, Dejun
AU - Tang, Jian
N1 - Publisher Copyright:
© 1983-2012 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/2
Y1 - 2017/2
N2 - 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.
AB - 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.
KW - Game theory
KW - crowdsourcing
KW - false-name proofness
KW - incentive mechanism
KW - truthfulness
UR - http://www.scopus.com/inward/record.url?scp=85017312612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017312612&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2017.2659229
DO - 10.1109/JSAC.2017.2659229
M3 - Article
AN - SCOPUS:85017312612
VL - 35
SP - 478
EP - 485
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
SN - 0733-8716
IS - 2
M1 - 7835128
ER -