Secure and PrivAte Collaborative EnvironmentS (SPACES) for biomedical analytics
Secure and PrivAte Collaborative EnvironmentS (SPACES) for biomedical analytics
批准号:
9239403
负责人:
Jaideep Vaidya
金额:
$36.88万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-01-01 至 2019-12-31
关键词:
AddressAdverse effectsAlgorithmic AnalysisAlgorithmsBig DataBig Data to KnowledgeBiomedical ResearchCaringChronic DiseaseClinicComplexDataData AnalysesData CollectionData ProtectionData SetDecision TreesDiabetes MellitusDiseaseDrug InteractionsEconomicsGoalsGrantHandHealthcareInformaticsInterventionJointsKnowledgeLeadMalignant NeoplasmsMeasurementMeasuresMedicalMental DepressionMethodologyModelingMonitorOntologyOutcome MeasurePatientsPatternPrincipal InvestigatorPrivacyPrivatizationProcessPsychiatryPublic HealthResearchResearch DesignResearch PersonnelResearch Project GrantsRiskRisk AssessmentSafetySamplingSchemeSecureSecuritySemanticsSoftware ToolsSystemTechniquesTechnologyTestingTranslational ResearchValidationWorkbasebiomedical informaticscollaborative environmentcomputer based Semantic Analysiscostdata accessdata managementflexibilityhealth care availabilityimprovedinsightnovel diagnosticsoncologyopen sourceprogramspublic health interventionpublic health researchrepositorysocialtooltool developmentusability
中文摘要
生物医学分析的安全和私人协作环境
英文摘要
Secure and Private Collaborative Environments for Biomedical Analytics
Privacy and confidentiality are critical to healthcare. However, preserving privacy
is a non-trivial task because any protection scheme essentially involves a
tradeoff with data utility. Furthermore, lack of access to biomedical data can lead
to fragmentation of care, resulting in higher economic and social costs, higher
safety risk from avoidable drug interactions side-effects, and is a significant
impediment to biomedical research. The primary aim of this project is to facilitate
biomedical research in collaborative environments by developing technologies for
secure and privacy-preserving exploratory analysis. This is critical to the big data
to knowledge (BD2K) initiative since it can facilitate biomedical research in
collaborative environments.
The proposed work addresses two complementary aims. First, we will develop
technologies that enable exploratory analysis of data to determine its usability
and relevance to the specific biomedical research task. We will also develop
technologies to enable the measurement and mitigation of additional
privacy/security risk due to accessing this data, thus enabling proper control over
the data. The proposed solutions will utilize and enhance the state of the art
technological solutions such as privacy-preserving sampling and analysis
algorithms, risk-based access control, and query auditing. By doing so, the
proposed work will enhance biomedical study design, exploratory data analysis,
and hypothesis discovery without compromising on privacy.
The project will result in open-source, freely available software tools to perform
utility analysis and risk analysis. These will be integrated into REDCap, a data
collection and management system used widely for providing translational
research informatics support. Several ongoing projects at Rutgers and UCSD will
serve as initial pilot customers for the proposed work, with other research groups
also being involved at a later stage.
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会议论文
Developing novel technologies that ensure privacy and security in biomedical data science research
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批准号:10321920
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项目类别:
-
资助金额:$38.45万
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财政年份:2020
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负责人:Jaideep Vaidya
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依托单位:
Supplement for high performance compute clusters
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批准号:10582256
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项目类别:
-
资助金额:$18.3万
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财政年份:2020
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负责人:Jaideep Vaidya
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依托单位:
Developing novel technologies that ensure privacy and security in biomedical data science research
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批准号:10560470
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项目类别:
-
资助金额:$38.45万
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财政年份:2020
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负责人:Jaideep Vaidya
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依托单位:
海外基金