HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

DOI10.1080/00949655.2018.1545228OpenAlexW2901178224WikidataQ59795226 ScholiaQ59795226MaRDI QIDQ5107323

No author found.

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6450541

zbMATH Keywords

big datadata sharinginformation protectionstatistical methodpersonal privacy

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

DataSifter

Uses Software

• missForest
• MovieLens

Cites Work

• Properties of Euclidean and non-Euclidean distance matrices
• Adjusting the penalized term for the regularized regression models
• Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based
• Implementing Gentry’s Fully-Homomorphic Encryption Scheme
• What Can We Learn Privately?
• The Algorithmic Foundations of Differential Privacy
• Differential Privacy: A Survey of Results
• Fully Homomorphic Encryption over the Integers
• PrivBayes
• Fully homomorphic encryption using ideal lattices
• Some distance properties of latent root and vector methods used in multivariate analysis
• Theory of Cryptography

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