Resilient Participant Selection Under Vulnerability Induced Colluding Attacks for Crowdsourcing

This paper investigates the problem of participant selection considering colluding attacks for crowdsourcing. Compared with existing work, a practical vulnerability-induced colluding attack model is considered, where the malicious participants with different vulnerability levels collude with each other to perform attacks, which makes the participant selection problem more challenging. To address this problem, according to the structural characteristics of the colluding attack model, we derive the necessary condition and sufficient condition of achieving the colluding possibility minimization of the selected participants. A novel resilient participant selection algorithm is developed based on the necessary condition and sufficient condition. The proposed algorithm can select participants to complete tasks with the time complexity
$O(mn)$
, where
$m, n$
are the numbers of participants and tasks, respectively. We analyze that the proposed algorithm achieves the colluding possibility minimization to defeat the vulnerability-induced colluding attack. It is also analyzed that the social cost of the proposed algorithm is smaller than existing algorithms in terms of the task-performing cost and the potential damage of the colluding attacks. Extensive real-world trace-based simulations are conducted to demonstrate the effectiveness of the proposed algorithm and the correctness of the theoretical results.