Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866916120414388224 |
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| author | Hoseinpour, Mohammad Hoseinpour, Milad Aghagolzadeh, Ali |
| author_facet | Hoseinpour, Mohammad Hoseinpour, Milad Aghagolzadeh, Ali |
| contents | Machine learning (ML) models can memorize training datasets. As a result, training ML models over private datasets can lead to the violation of individuals' privacy. Differential privacy (DP) is a rigorous privacy notion to preserve the privacy of underlying training datasets. Yet, training ML models in a DP framework usually degrades the accuracy of ML models. This paper aims to boost the accuracy of a DP logistic regression (LR) via a pre-training module. In more detail, we initially pre-train our LR model on a public training dataset that there is no privacy concern about it. Then, we fine-tune our DP-LR model with the private dataset. In the numerical results, we show that adding a pre-training module significantly improves the accuracy of the DP-LR model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_13771 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach Hoseinpour, Mohammad Hoseinpour, Milad Aghagolzadeh, Ali Machine Learning Cryptography and Security Machine learning (ML) models can memorize training datasets. As a result, training ML models over private datasets can lead to the violation of individuals' privacy. Differential privacy (DP) is a rigorous privacy notion to preserve the privacy of underlying training datasets. Yet, training ML models in a DP framework usually degrades the accuracy of ML models. This paper aims to boost the accuracy of a DP logistic regression (LR) via a pre-training module. In more detail, we initially pre-train our LR model on a public training dataset that there is no privacy concern about it. Then, we fine-tune our DP-LR model with the private dataset. In the numerical results, we show that adding a pre-training module significantly improves the accuracy of the DP-LR model. |
| title | Accuracy Improvement in Differentially Private Logistic Regression: A Pre-training Approach |
| topic | Machine Learning Cryptography and Security |
| url | https://arxiv.org/abs/2307.13771 |