Cryptanalytic Extraction of Neural Networks for Privacy-Preserving Machine Learning

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Since Carlini et al. (CRYPTO'20) introduced a black-box neural network extraction method inspired by differential cryptanalysis, cryptanalytic model extraction has been extensively studied. Existing methods, however, still face three bottlenecks. \begin{itemize} \item First, these methods are largely confined to contractive architectures and generally do not extend to expansive networks because of rank deficiency. We classify the regimes for single-point and multi-point recovery and mitigate