
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.


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Deciphering the Interplay between Local Differential Privacy, Average Bayesian Privacy, and Maximum Bayesian Privacy


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Chernoff information is compared to Kullback-Leibler divergence for privacy metrics in adversarial classification.


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Chernoff Information and Adversarial Classification Privacy Metrics Comparison
