Reconciling privacy and utility: an unscented Kalman filter-based framework for differentially private machine learning

DOI10.1007/s10994-022-06279-5OpenAlexW4311834320MaRDI QIDQ6097151

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Publication date: 12 June 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06279-5

zbMATH Keywords

machine learningdifferential privacyunscented Kalman filterinference attacksprivacy-utility reconcilement

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

• Randomized approximate class-specific kernel spectral regression analysis for large-scale face verification
• Differential privacy under continual observation
• On the Relation Between Identifiability, Differential Privacy, and Mutual-Information Privacy
• The Algorithmic Foundations of Differential Privacy
• Large-Scale Machine Learning with Stochastic Gradient Descent
• Differential Privacy: A Survey of Results
• Our Data, Ourselves: Privacy Via Distributed Noise Generation
• A deep learning approach using natural language processing and time-series forecasting towards enhanced food safety
• Unnamed Item

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