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Analyzing Inference Privacy Risks Through Gradients In Machine Learning

(2024)10.1145/3658644.3690304Source: DataRank Database

Analyzing Inference Privacy Risks Through Gradients In Machine Learning is a research paper (2024). On theSindex it has a DataRank of 0. It has been cited 7 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access7 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (2 grants)OA: goldTopics (9)IDs (PubMed)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (7)

Andrew Lowy,Jing Liu ORCID,Toshiaki Koike–Akino ORCID,Kieran Parsons ORCID,Bradley Malin ORCID

Live enrichment

PubMed:19767382

National Institute of Health

Grant: U54HG012510

National Institutes of Health

Grant: 1U54HG012510-01

A FAIR Bridge2AI Center (FABRIC)

goldLicense: https://www.acm.org/publications/policies/copyright_policy#Background

https://dl.acm.org/doi/pdf/10.1145/3658644.3690304

publisherhttps://dl.acm.org/doi/10.1145/3658644.3690304

https://doi.org/10.1145/3658644.3690304

https://dx.doi.org/10.48550/arxiv.2408.16913

http://arxiv.org/abs/2408.16913

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningComputer Science - Cryptography and SecurityArtificial Intelligence (cs.AI)Computer Science - Artificial IntelligenceStatistics - Machine LearningMachine Learning (stat.ML)Cryptography and Security (cs.CR)Machine Learning (cs.LG)