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Assemblage: Automatic Binary Dataset Construction for Machine Learning
Our results illustrate the practical need for robust corpuses of high-quality Windows PE binaries in training modern learning-based binary analyses.
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile
Differential privacy (DP) is the prevailing technique for protecting user data in machine learning models.
A General Framework for Auditing Differentially Private Machine Learning
We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice.
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