Min Entropy

Min-entropy, a measure of uncertainty in a random variable, is crucial for applications requiring strong randomness, such as cryptography and secure communication. Current research focuses on improving min-entropy estimation techniques, particularly for random number generators, using machine learning models like convolutional neural networks, recurrent neural networks (LSTMs), and transformers, often comparing their performance against traditional methods. These advancements aim to enhance the security and reliability of systems dependent on high-quality random number generation, and also extend to other fields like domain adaptation in graph-based machine learning and robust statistical signal processing. Furthermore, min-entropy is being explored in novel applications such as perfectly secure steganography and trajectory inference.