Differentially Private Projected Histograms: Construction and Use for Prediction

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Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing (2014, positive): “In order to select the number of bins, we use a heuristic given by Lei [32] and suggested by Vinterbo [44], which says that when numeric attributes are scaled to the interval [0,1], the bin width is given by (log(n)/n)1/(d+1), where n = |D| and d is the dimension of D.”
Towards Making Systems Forget with Machine Unlearning (2015, neutral): “I. INTRODUCTION”
A Stability-based Validation Procedure for Differentially Private Machine Learning (2013, background): “…over the past few years as there has been a large explosion of theoretical and experimental work on differentially private algorithms for statistical and machine-learning tasks [10, 2, 15, 19, 27, 28, 3] – see [24] for a recent survey of machine learning methods with a focus on continuous data.”
Differential Privacy and Machine Learning: a Survey and Review (2014, background): “Other papers [45, 50, 6, 26, 21, 31, 7, 53, 27] give bounds on how fast the output models converge to true models.”