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It uses differential privacy to obfuscate responses for queries that fall near a model's decision boundary.
1. "BDPL: A Boundary Differentially Private Layer Against Machine Learning Model Extraction Attacks" bdplarchive.rar
This is the most probable match. Published in (European Symposium on Research in Computer Security), this paper introduces a security layer designed to protect machine learning models from being "stolen" or extracted by adversaries. It uses differential privacy to obfuscate responses for