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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2309.16398 |
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| _version_ | 1866912621273284608 |
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| author | Demelius, Lea Kern, Roman Trügler, Andreas |
| author_facet | Demelius, Lea Kern, Roman Trügler, Andreas |
| contents | Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: auditing and evaluation methods for private models, improvements of privacy-utility trade-offs, protection against a broad range of threats and attacks, differentially private generative models, and emerging application domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16398 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey Demelius, Lea Kern, Roman Trügler, Andreas Machine Learning Differential Privacy has become a widely popular method for data protection in machine learning, especially since it allows formulating strict mathematical privacy guarantees. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning, thorough analyses of recent advances and open problems, as well as a discussion of potential future developments in the field. Based on a systematic literature review, the following topics are addressed: auditing and evaluation methods for private models, improvements of privacy-utility trade-offs, protection against a broad range of threats and attacks, differentially private generative models, and emerging application domains. |
| title | Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2309.16398 |