Inherent Privacy

Inherent privacy in machine learning focuses on designing systems and algorithms that protect sensitive data without relying solely on post-hoc privacy-preserving techniques like differential privacy. Current research emphasizes developing models with built-in privacy features, exploring architectures like graph neural networks and quantum circuits, and analyzing the privacy properties of diffusion models and transformers. This field is crucial for enabling the responsible use of machine learning in sensitive applications, addressing concerns about data leakage and improving the trustworthiness of AI systems.