Secure outsourced computation of genomic data
Secure outsourced computation of genomic data
批准号:
9906292
负责人:
Aziz A Boxwala
金额:
$34.49万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-09 至 2021-08-31
关键词:
AgeAlgorithmsAllelesArchivesAwarenessCaringClientClinicalClinical DataClinical TrialsCloud ComputingCommunicationComputer softwareConsumptionDataData AnalysesData ProtectionData ReportingData SecurityDisclosureDiseaseElectronic Health RecordEligibility DeterminationEvolutionGenesGeneticGenomeGenomicsGenotypeGoalsGuidelinesHealth SciencesHealth care facilityHealthcareHospitalsHumanIndividualInvestmentsLearningLinkLongevityMeasurementMeasuresMedicineMemoryNatureOutsourcingPatient Data PrivacyPatientsPerceptionPerformancePersonsPharmaceutical PreparationsPharmacogenomicsPhasePredispositionPrivacyProtocols documentationRecommendationResearchRestRiskSecureSecurityServicesSmall Business Technology Transfer ResearchSpeedSystemTechniquesTechnologyTestingTexasTimeUniversitiesVariantbaseclinical applicationclinical careclinical decision supportclinical decision-makingclinical practicecloud basedcost efficientdata formatdisorder riskempoweredencryptiongenetic variantgenomic datagigabytehealth care service organizationnovelpatient privacyprecision medicinepreservationprivacy protectionresponserural areasoftware systemsvector
中文摘要
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英文摘要
Project Summary
In the age of precision medicine, genomic data are being integrated with other health care data
to support personalized and calibrated clinical decision-making. Genomic sequence data are too
large to be stored in electronic health record (EHR) systems and need to be separately stored.
While cloud computing offers a cost-efficient and scalable platform, the privacy and security
concerns about outsourcing genomic data are challenging issues. The common perception is
that the ease of access to remote data and the protection of privacy are at odds with each other.
We propose a new genomics archiving and communications system (GACS) that meets both
requirements by using state-of-the-art homomorphic encryption algorithms and matrix
representation of data and queries.
In this system, variants are represented as vectors, that are homomorphically encrypted by a
client and stored on the GACS server. When analysis is required, a query is generated in the
form of a matrix. This matrix is encrypted (or can remain in plaintext depending on the task) and
sent to the GACS server. The server computes on encrypted data, produces an encrypted result
and returns it to the client, who has the secret key to decode it. The GACS is not able to decrypt
the data or the encrypted queries, thus guaranteeing that privacy and security are maintained
on the GACS. Preliminary results of the algorithms show that after decryption, the results are
the same as results from computing on plaintext. In this project, we will implement our GACS
system software modules and demonstrate the use of the system with examples from three use-
cases: pharmacogenomics, clinical trials eligibility and analysis for disease risks. We will
measure performance speed and memory consumption in all three use-cases.
A GACS system as a cloud-hosted service can reduce the computational burden on healthcare
facilities. It can provide small healthcare facilities with the same genomic analysis capability
available to larger hospitals. In addition, clinical decision support (CDS) can be deployed on the
GACS. As clinical guidelines evolve in response to new discoveries linking genetic variants to
disease and medicines, healthcare facilities can stay in compliance with the guidelines.
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