@inproceedings{
li2024learning,
title={Learning from Concealed Labels},
author={Zhongnian Li and Meng Wei and Peng Ying and Tongfeng Sun and Xinzheng Xu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=a6vvqnXLIU}
}
Annotating data for sensitive labels (e.g., disease,  smoking)  poses a  potential threats to individual privacy in many real-world scenarios.   To  cope with this problem,  we  propose a novel setting to protect  privacy of each  instance, namely learning from concealed labels for multi-class classification.  Concealed labels prevent sensitive labels from appearing in the label set during the label collection  stage, as shown in Figure 1, which specifies none  and  some random sampled  insensitive labels  as concealed labels set to annotate sensitive data. In this paper, an unbiased estimator can be established from concealed data under mild assumptions, and the learned multi-class classifier can not only classify the instance from insensitive labels accurately but also recognize the instance from the sensitive labels. Moreover, we bound the estimation error and show that the multi-class classifier achieves the optimal parametric convergence rate. Experiments demonstrate the significance and effectiveness of the proposed method for concealed labels in synthetic and real-world datasets.
Learning from Concealed Labels
Zhongnian Li Meng Wei Peng Ying Tongfeng Sun Xinzheng Xu