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Label differential privacy is a relaxation of differential privacy for machine learning scenarios where the labels are the only sensitive information that needs to be protected in the training data. For example, imagine a survey from a participant in a university class about their vaccination status. Some attributes of the students are publicly available but their vaccination status is sensitive information and must remain private. Now if we want to train a model that predicts whether a student has received vaccination using only their public information, we can use label-DP. Recent works on label-DP use different ways of adding noise to the labels in order to obtain label-DP models. In this work, we present novel techniques for training models with label-DP guarantees by leveraging unsupervised learning and semi-supervised learning, enabling us to inject less noise while obtaining the same privacy, therefore achieving a better utility-privacy trade-off. We first introduce a framework that starts with an unsupervised classifier f0 and dataset D with noisy label set Y , reduces the noise in Y using f0 , and then trains a new model f using the less noisy dataset. Our noise reduction strategy uses the model f0 to remove the noisy labels that are incorrect with high probability. Then we use semi-supervised learning to train a model using the remaining labels. We instantiate this framework with multiple ways of obtaining the noisy labels and also the base classifier. As an alternative way to reduce the noise, we explore the effect of using unsupervised learning: we only add noise to a majority voting step for associating the learned clusters with a cluster label (as opposed to adding noise to individual labels); the reduced sensitivity enables us to add less noise. Our experiments show that these techniques can significantly outperform the prior works on label-DP.
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This content will become publicly available on May 7, 2026
Retraining with Predicted Hard Labels Provably Increases Model Accuracy
Training with noisy labels often yields suboptimal performance, but retraining a model with its own predicted hard labels (binary 1/0 outputs) has been empirically shown to improve accuracy. This paper provides the first theoretical characterization of this phenomenon. In the setting of linearly separable binary classification with randomly corrupted labels, the authors prove that retraining can indeed improve the population accuracy compared to initial training with noisy labels. Retraining also has practical implications for local label differential privacy (DP), where models are trained with noisy labels. The authors propose consensus-based retraining, where retraining is done selectively on samples for which the predicted label matches the given noisy label. This approach significantly improves DP training accuracy at no additional privacy cost. For example, training ResNet-18 on CIFAR-100 with ε = 3 label DP achieves over 6% accuracy improvement with consensus-based retraining.
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- Award ID(s):
- 2505865
- PAR ID:
- 10631934
- Publisher / Repository:
- https://doi.org/10.48550/arXiv.2406.11206
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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