Dataset Level Privacy

Dataset-level privacy in machine learning focuses on safeguarding sensitive information within training data while maintaining model accuracy. Current research emphasizes developing techniques to enhance model stability, reducing the need for strong privacy-preserving mechanisms that often compromise performance, and exploring novel dimensionality reduction methods to minimize data exposure before applying privacy-preserving algorithms like differential privacy. This area is crucial for responsible AI development, enabling the use of sensitive data in various applications like fraud detection and healthcare while mitigating privacy risks.