Privacy Loss

Privacy loss in machine learning focuses on quantifying and mitigating the risk of revealing sensitive information during model training and inference. Current research emphasizes developing tighter privacy accounting methods, particularly for adaptive algorithms and complex models like large language models, often employing techniques like Rényi differential privacy and Gaussian differential privacy to achieve better accuracy while maintaining strong privacy guarantees. This work is crucial for enabling the responsible deployment of powerful machine learning systems across various applications, balancing the benefits of advanced algorithms with the need to protect individual privacy. Improved privacy accounting techniques are leading to more efficient and accurate privacy-preserving machine learning.