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)O(mn), where m,nm, 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.

中文翻译：

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漏洞引发的众包共谋攻击下的弹性参与者选择


本文研究了考虑众包共谋攻击的参与者选择问题。与现有工作相比，考虑了一种实用的漏洞诱导共谋攻击模型，其中具有不同漏洞级别的恶意参与者相互共谋进行攻击，这使得参与者选择问题更具挑战性。针对这一问题，根据共谋攻击模型的结构特点，推导了实现所选参与者共谋可能性最小化的必要条件和充分条件。基于必要条件和充分条件，开发了一种新颖的弹性参与者选择算法。该算法可以选择参与者来完成任务，时间复杂度为O(mn)O(mn)，其中m、nm、n分别是参与者和任务的数量。我们分析发现，所提出的算法实现了共谋可能性最小化，从而击败了漏洞引发的共谋攻击。分析发现，在任务执行成本和共谋攻击的潜在损害方面，该算法的社会成本小于现有算法。进行了广泛的基于轨迹的现实世界仿真，以证明所提出算法的有效性和理论结果的正确性。