Understanding the relationship between Local Differential Privacy (LDP), Average Bayesian Privacy (ABP), and Maximum Bayesian Privacy (MBP) is crucial for developing robust privacy-preserving algorithms in machine learning.
Chernoff information is compared to Kullback-Leibler divergence for privacy metrics in adversarial classification.