An Overview of Privacy-Preserving and Security Methods for Deep Learning

The advent of deep learning has revolutionized diverse domains, showcasing remarkable performance in tasks such as image recognition, natural language processing, and autonomous systems. Despite its widespread adoption and myriad advantages, concerns about privacy and security loom large, especially when handling sensitive data. This research paper surveys the challenges, proposes countermeasures, and sheds light on open issues and potential threats within the industry. We present a comprehensive overview of privacy-preserving and security methods for deep learning, addressing the deployment challenges in privacy-sensitive applications.