Learning from Concealed Labels

Zhongnian Li, Meng Wei, Peng Ying, Tongfeng Sun, Xinzheng Xu

Abstract: 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.

Primary Subject Area: [Systems] Data Systems Management and Indexing

Relevance To Conference:

A novel machine learning setting for protecting the privacy of multimedia data.
We propose a novel private-label weakly supervised learning setting, i.e., learning from concealed labels, which prevents sensitive labels from appearing in the label set.
We propose an empirical risk minimization method that constructs an unbiased estimator for multi-class classification using concealed labels data, and provides estimation error bounds for the proposed method.
We experimentally demonstrate that the learned classifier is useful for recognizing instances from both unconcealed and conceal labels on various benchmark datasets and two real-world concealed labels datasets.

Submission Number: 1282