Abstract
Despite its successes in various machine learning and data science tasks, crowdsourcing can be susceptible to attacks from dedicated adversaries. This work investigates the effects of adversaries on crowdsourced classification, under the popular Dawid and Skene model. The adversaries are allowed to deviate arbitrarily from the considered crowdsourcing model, and may potentially cooperate. To address this scenario, we develop an approach that leverages the structure of second-order moments of annotator responses, to identify large numbers of adversaries, and mitigate their impact on the crowdsourcing task. The potential of the proposed approach is empirically demonstrated on synthetic and real crowdsourcing datasets.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 21st IEEE International Conference on Data Mining, ICDM 2021 |
| Editors | James Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1373-1378 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781665423984 |
| DOIs | |
| State | Published - 2021 |
| Event | 21st IEEE International Conference on Data Mining, ICDM 2021 - Virtual, Online, New Zealand Duration: Dec 7 2021 → Dec 10 2021 |
Publication series
| Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
|---|---|
| Volume | 2021-December |
| ISSN (Print) | 1550-4786 |
Conference
| Conference | 21st IEEE International Conference on Data Mining, ICDM 2021 |
|---|---|
| Country/Territory | New Zealand |
| City | Virtual, Online |
| Period | 12/7/21 → 12/10/21 |
Bibliographical note
Funding Information:Work in this paper was supported by NSF grants 1901134, 2126052, 2128593 and ARO-STIR grant 00093896.
Publisher Copyright:
© 2021 IEEE.
Keywords
- Adversaries
- Classification
- Crowdsourcing
- Ensemble learning