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Data · dataset · 2019

PPCD: Privacy-preserving clinical decision with cloud support

Listed in BERD@NFDI Data Portal

With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients.

Description

However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient.

Each party learns nothing about the other's private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure.

Links

Where it is published

Catalogue records · 1

Topics

From keywords
Economics & Finance
Inferred from text
Disease 75%
Provenance · 1 source records, 7 field assertions
SourceKeyLast seenRaw
BERD@NFDI Data Portalr6zcx-7kf9011 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · berd platform deconnector:berd_platform_de@1.0.0
concepts[disease].local:disease:diseaseenrichment · berd platform dekeyword-concept-rules@1.0.0title+description (75%)
concepts[field].local:field:economics-financemapping · berd platform deconnector:berd_platform_de@1.0.0
descriptionsource · berd platform deconnector:berd_platform_de@1.0.0/metadata/description
publication_datesource · berd platform deconnector:berd_platform_de@1.0.0
titlesource · berd platform deconnector:berd_platform_de@1.0.0/metadata/title
updated_datesource · berd platform deconnector:berd_platform_de@1.0.0