Analysis of Application Examples of Differential Privacy in Deep Learning

Area of Science:

• Computer Science
• Artificial Intelligence
• Cybersecurity

Background:

• Artificial Intelligence (AI) applications raise privacy concerns due to data leakage.
• Model inference attacks on deep neural networks (DNNs) can extract sensitive user information.
• Protecting privacy in deep learning is crucial.

Purpose of the Study:

• To comprehensively analyze and compare different differential privacy methods applied to deep learning.
• To classify deep learning models under differential privacy.
• To examine the application of differential privacy in Generative Adversarial Networks (GANs).

Main Methods:

• Comparative analysis of various differential privacy techniques in deep learning.
• Classification of deep learning models incorporating differential privacy.
• Specific analysis of differential privacy in Generative Adversarial Networks (GANs).

Main Results:

• Identified and categorized different differential private deep learning models.
• Evaluated the effectiveness and differences of various privacy-preserving methods.
• Detailed comparison of differential privacy applications in GANs.

Conclusions:

• Differential privacy offers a robust framework for privacy preservation in deep learning.
• Understanding the nuances of different methods is key for effective implementation.
• Further research can enhance privacy guarantees in AI systems.