Privacy Budget

Privacy budgets are mechanisms for controlling the amount of information leaked when training machine learning models on sensitive data, balancing privacy protection with model utility. Current research focuses on optimizing budget allocation strategies across different model training iterations, data points, or even features, often employing adaptive methods and exploring various differential privacy mechanisms within federated learning and deep learning frameworks. These advancements aim to improve the accuracy and fairness of privacy-preserving machine learning, impacting fields like healthcare and social sciences where data privacy is paramount. The ultimate goal is to develop techniques that minimize privacy leakage while maximizing the utility of the trained models.