Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey

• URL: http://arxiv.org/abs/2309.16398v1
• Date: Thu, 28 Sep 2023 12:44:59 GMT
• Title: Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
• Authors: Lea Demelius, Roman Kern, Andreas Tr\"ugler
• Abstract summary: Differential Privacy has become a widely popular method for data protection in machine learning. This survey provides an overview of the state-of-the-art of differentially private centralized deep learning.
• Score: 1.89915151018241
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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