Enhancing Learning with Label Differential Privacy by Vector Approximation
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929357356793856 |
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| author | Zhao, Puning Fan, Rongfei Wu, Huiwen Li, Qingming Wu, Jiafei Liu, Zhe |
| author_facet | Zhao, Puning Fan, Rongfei Wu, Huiwen Li, Qingming Wu, Jiafei Liu, Zhe |
| contents | Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the privacy of labels by flipping them randomly, and then train a model to make the output approximate the privatized label. However, as the number of classes $K$ increases, stronger randomization is needed, thus the performances of these methods become significantly worse. In this paper, we propose a vector approximation approach, which is easy to implement and introduces little additional computational overhead. Instead of flipping each label into a single scalar, our method converts each label into a random vector with $K$ components, whose expectations reflect class conditional probabilities. Intuitively, vector approximation retains more information than scalar labels. A brief theoretical analysis shows that the performance of our method only decays slightly with $K$. Finally, we conduct experiments on both synthesized and real datasets, which validate our theoretical analysis as well as the practical performance of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15150 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhancing Learning with Label Differential Privacy by Vector Approximation Zhao, Puning Fan, Rongfei Wu, Huiwen Li, Qingming Wu, Jiafei Liu, Zhe Machine Learning Label differential privacy (DP) is a framework that protects the privacy of labels in training datasets, while the feature vectors are public. Existing approaches protect the privacy of labels by flipping them randomly, and then train a model to make the output approximate the privatized label. However, as the number of classes $K$ increases, stronger randomization is needed, thus the performances of these methods become significantly worse. In this paper, we propose a vector approximation approach, which is easy to implement and introduces little additional computational overhead. Instead of flipping each label into a single scalar, our method converts each label into a random vector with $K$ components, whose expectations reflect class conditional probabilities. Intuitively, vector approximation retains more information than scalar labels. A brief theoretical analysis shows that the performance of our method only decays slightly with $K$. Finally, we conduct experiments on both synthesized and real datasets, which validate our theoretical analysis as well as the practical performance of our method. |
| title | Enhancing Learning with Label Differential Privacy by Vector Approximation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.15150 |