TY - GEN
T1 - Sensitive task assignments in crowdsourcing markets with colluding workers
AU - Sun, Haipei
AU - Dong, Boxiang
AU - Zhang, Bo
AU - Wang, Wendy Hui
AU - Kantarcioglu, Murat
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/24
Y1 - 2018/10/24
N2 - Crowdsourcing has raised several security concerns. One of the concerns is how to assign sensitive tasks in the crowdsourcing market, especially when there are colluding participants in crowdsourcing. In this paper, we consider adversarial colluding participants who intend to extract sensitive data by exchanging information. We design a 3-step sensitive task assignment method: (1) the collusion estimation step that quantifies the workers' pairwise collusion probability by estimating answer truth based on their responses; (2) the worker selection step that executes a heuristic sampling-based approach to select the fewest workers whose collusion probability satisfies the given security requirement; and (3) the task partitioning step that splits the sensitive information among the selected workers. We perform an extensive set of experiments on both real-world and synthetic datasets. The results demonstrate the accuracy and efficiency of our method.
AB - Crowdsourcing has raised several security concerns. One of the concerns is how to assign sensitive tasks in the crowdsourcing market, especially when there are colluding participants in crowdsourcing. In this paper, we consider adversarial colluding participants who intend to extract sensitive data by exchanging information. We design a 3-step sensitive task assignment method: (1) the collusion estimation step that quantifies the workers' pairwise collusion probability by estimating answer truth based on their responses; (2) the worker selection step that executes a heuristic sampling-based approach to select the fewest workers whose collusion probability satisfies the given security requirement; and (3) the task partitioning step that splits the sensitive information among the selected workers. We perform an extensive set of experiments on both real-world and synthetic datasets. The results demonstrate the accuracy and efficiency of our method.
KW - Collusion detection
KW - Crowdsourcing
KW - Task assignment
UR - http://www.scopus.com/inward/record.url?scp=85057084395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85057084395&partnerID=8YFLogxK
U2 - 10.1109/ICDE.2018.00042
DO - 10.1109/ICDE.2018.00042
M3 - Conference contribution
AN - SCOPUS:85057084395
T3 - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
SP - 377
EP - 388
BT - Proceedings - IEEE 34th International Conference on Data Engineering, ICDE 2018
T2 - 34th IEEE International Conference on Data Engineering, ICDE 2018
Y2 - 16 April 2018 through 19 April 2018
ER -