Abstract
Crowdsourcing is an effective paradigm for recruiting online workers to perform intelligent tasks that are difficult for computers to complete. More and more attacks bring challenges to crowdsourcing systems. Although the truth discovery method can defend against common attacks to a certain extent, the real scene is much more complex. Malicious workers can not only improve their reliability by agreeing with normal workers on tasks that are unlikely to be overturned, but also gather together to launch more effective attacks on tasks that are easily overturned. This disguised attack is smarter and harder to defend. To solve this problem, we propose a new defense framework TD-DA (Truth Discovery against Disguised Attack) composed of truth discovery and task allocation. In the truth discovery phase, we quantify the aggressiveness and reliability of workers on the golden task based on the sigmoid function. In the task allocation phase, the Weighted Arithmetic Mean (WAM) is used to estimate the allocation probability of golden tasks to avoid the shortage of golden tasks. Extensive experiments on real-world datasets and synthetic datasets demonstrate that our method is effective against disguised attacks.
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Acknowledgement
This work was supported by Fundamental Research Funds for the Central Universities (No. 23D111204, 22D111210), Shanghai Science and Technology Commission (No. 22YF1401100), and National Science Fund for Young Scholars (No. 62202095).
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Fang, X., Tang, Y., Sun, G., Shen, C., Chen, H. (2024). Truth Discovery Against Disguised Attack Mechanism in Crowdsourcing. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14333. Springer, Singapore. https://doi.org/10.1007/978-981-97-2387-4_5
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DOI: https://doi.org/10.1007/978-981-97-2387-4_5
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