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A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations

The Journal of Machine Learning for Biomedical Imaging(2022)10.59275/j.melba.2022-7175Source: DataRank Database

A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations is a research paper published in The Journal of Machine Learning for Biomedical Imaging (2022). On theSindex it has a DataRank of 0. It has been cited 3 times.

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Open Access3 citations

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A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations · arXiv

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)Impact (FWCI 0.34)OA: bronzeTopics (29)Linked Datasets (1)DataCite Reuse (1)IDs (PubMed, OpenAlex)

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Context only (not used in score)

Findable (2/2)

Has DOI
Indexed in repositories

Accessible (1/2)

Open Access

Interoperable (2/2)

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Reusable (0/3)

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Authors (3)

Santiago Silva ORCID,Marco Lorenzi ORCID,Irène Balelli ORCID

Live enrichment

PubMed:21097893 OpenAlex:https://openalex.org/W4283735188

Canadian Institutes of Health Research

Grant: unidentified

unidentified

National Institutes of Health

Grant: 1U01AG024904-01

Alzheimers Disease Neuroimaging Initiative

French National Research Agency (ANR)

Grant: ANR-19-P3IA-0002

FWCI

0.34

Citation Percentile

0.6%

Citation Trend

2022

2023

bronzeLicense: CC BY

journalhttps://www.melba-journal.org/pdf/2022:012.pdf

publisherhttps://www.melba-journal.org/pdf/2022:012.pdf

publisherhttp://melba-journal.org/pdf/2022:012.pdf

journalhttps://doi.org/10.59275/j.melba.2022-7175

https://dx.doi.org/10.48550/arxiv.2204.07352

Fields of Study

Privacy-Preserving Technologies in DataMRI in cancer diagnosisStatistical Methods and Inference02 engineering and technology0202 electrical engineering, electronic engineering, information engineering

Keywords

Computer scienceDifferential privacyProbabilistic logicRealization (probability)Bayesian probabilityLatent variableData miningCode (set theory)Federated learningMachine learningArtificial intelligenceStatisticsFOS: Computer and information sciencesComputer Science - Machine LearningStatistics - Applications[STAT.ML] Statistics [stat]/Machine Learning [stat.ML]Machine Learning (cs.LG)Methodology (stat.ME)[STAT.AP] Statistics [stat]/Applications [stat.AP]Computer Science - Distributed, Parallel, and Cluster Computing[INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC]Applications (stat.AP)Distributed, Parallel, and Cluster Computing (cs.DC)Statistics - Methodology

A Differentially Private Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations

10.48550/arxiv.2204.07352arXiv