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Predicting worker disagreement for more effective crowd labeling

, , , and . 2018 IEEE 5th International Conference on Data Science andAdvanced Analytics (DSAA), page 179--188. IEEE, (2018)

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Predicting worker disagreement for more effective crowd labeling, , , and . 2018 IEEE 5th International Conference on Data Science andAdvanced Analytics (DSAA), page 179--188. IEEE, (2018)Efficient Privacy Preserving Distributed Clustering Based on Secret Sharing, , , and . PAKDD 2007 International Workshops: Emerging Technologies in Knowledge Discovery and Data Mining, page 280--291. (2009)Recent advances in preserving privacy when mining data., , and . Data Knowl. Eng., 65 (1): 1-4 (2008)Anonymization of Longitudinal Electronic Medical Records., , , , and . IEEE Trans. Information Technology in Biomedicine, 16 (3): 413-423 (2012)Suppressing microdata to prevent classification based inference., and . VLDB J., 19 (3): 385-410 (2010)Sensitivity Analysis for Non-Interactive Differential Privacy: Bounds and Efficient Algorithms., , and . IEEE Trans. Dependable Secur. Comput., 17 (1): 194-207 (2020)Automated construction of fuzzy event sets and its application to active databases., and . IEEE Trans. Fuzzy Syst., 9 (3): 450-460 (2001)P2-CTM: privacy preserving collaborative traffic monitoring., , and . SPRINGL, page 55-59. ACM, (2010)Using Unknowns to Prevent Discovery of Association Rules., , and . SIGMOD Rec., 30 (4): 45-54 (2001)State-of-the-art in privacy preserving data mining., , , , , and . SIGMOD Rec., 33 (1): 50-57 (2004)