Zhou Jiyuan, Jin Xing, Yu Lanping, Xue Limin, Ren Yizhi
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China.
School of Information Engineering, Hangzhou Dianzi University, Hangzhou 310018, China.
Sensors (Basel). 2021 Apr 7;21(8):2578. doi: 10.3390/s21082578.
On a crowdsourcing platform, in order to cheat for rewards or sabotage the crowdsourcing processes, spam workers may submit numerous erroneous answers to the tasks published by requesters. This type of behavior extremely reduces the completion rate of tasks and the enthusiasm of honest users, which may lead a crowdsourcing platform to a failure. Defending against malicious attacks is an important issue in crowdsourcing, which has been extensively addressed by existing methods, e.g., verification-based defense mechanisms, data analysis solutions, trust-based defense models, and workers' properties matching mechanisms. However, verification-based defense mechanisms will consume a lot of resources, and data analysis solutions cannot motivate workers to provide high-quality services. Trust-based defense models and workers' properties matching mechanisms cannot guarantee the authenticity of information when collusion requesters publish shadow tasks to help malicious workers get more participation opportunities. To defend such collusion attacks in crowdsourcing platforms, we propose a new defense model named TruthTrust. Firstly, we define a complete life cycle system that from users' interaction to workers' recommendation, and separately define the trust value of each worker and the credence of each requester. Secondly, in order to ensure the authenticity of the information, we establish a trust model based on the CRH framework. The calculated truth value and weight are used to define the global properties of workers and requesters. Moreover, we propose a reverse mechanism to improve the resistance under attacks. Finally, extensive experiments demonstrate that TruthTrust significantly outperforms the state-of-the-art approaches in terms of effective task completion rate.
在众包平台上，为了骗取奖励或破坏众包流程，恶意工人可能会对请求者发布的任务提交大量错误答案。这种行为极大地降低了任务完成率和诚实用户的积极性，可能导致众包平台失败。抵御恶意攻击是众包中的一个重要问题，现有方法已广泛涉及，例如基于验证的防御机制、数据分析解决方案、基于信任的防御模型和工人属性匹配机制。然而，基于验证的防御机制会消耗大量资源，数据分析解决方案无法激励工人提供高质量服务。当勾结请求者发布影子任务以帮助恶意工人获得更多参与机会时，基于信任的防御模型和工人属性匹配机制无法保证信息的真实性。为了抵御众包平台中的此类勾结攻击，我们提出了一种名为TruthTrust的新防御模型。首先，我们定义了一个从用户交互到工人推荐的完整生命周期系统，并分别定义每个工人的信任值和每个请求者的可信度。其次，为了确保信息的真实性，我们基于CRH框架建立了一个信任模型。计算出的真值和权重用于定义工人和请求者的全局属性。此外，我们提出了一种反向机制来提高攻击下的抗性。最后，大量实验表明，TruthTrust在有效任务完成率方面显著优于现有方法。
