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cs.LG2024
Differential Privacy Under Class Imbalance: Methods and Empirical Insights
Lucas Rosenblatt, Yuliia Lut, Eitan Turok +2
Imbalanced learning occurs in classification settings where the distribution of class-labels is highly skewed in the training data, such as when predicting rare diseases or in frau…
cs.LG2023
Epsilon*: Privacy Metric for Machine Learning Models
Diana M. Negoescu, Humberto Gonzalez, Saad Eddin Al Orjany +9
We introduce Epsilon*, a new privacy metric for measuring the privacy risk of a single model instance prior to, during, or after deployment of privacy mitigation strategies. The me…