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Found 3 papers, 1 papers with code
Privacy-Preserving Collaborative Prediction using Random Forests
We study the problem of privacy-preserving machine learning (PPML) for ensemble methods, focusing our effort on random forests.
Exploring Connections Between Active Learning and Model Extraction
This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) - cloud services that provide (a) tools and resources to learn the model, and (b) a user-friendly query interface to access the model.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks.
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