2011
NIPS
NeurIPS 2011
Differentially Private M-Estimators
Abstract
This paper studies privacy preserving M-estimators using perturbed histograms. The proposed approach allows the release of a wide class of M-estimators with both differential privacy and statistical utility without knowing a priori the particular inference procedure. The performance of the proposed method is demonstrated through a careful study of the convergence rates. A practical algorithm is given and applied on a real world data set containing both continuous and categorical variables.
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Interdisciplinary Bridge
— Machine Learning and Security & Privacy
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Keyword Pioneer
— m-estimators
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Hot Topic Early Bird
— differential privacy
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
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Topic Pioneer
— Privacy
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Trend Setter
— Differential Privacy