A privacy protection method for health care big data management based on risk access control
With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the pro...
Gespeichert in:
Veröffentlicht in: | Health care management science 2020-09, Vol.23 (3), p.427-442 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 442 |
---|---|
container_issue | 3 |
container_start_page | 427 |
container_title | Health care management science |
container_volume | 23 |
creator | Shi, Mingyue Jiang, Rong Hu, Xiaohan Shang, Jingwei |
description | With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the problem of patient privacy leakage is the most serious. Therefore, strengthening the information management of intelligent health care in the era of big data is an important part of the long-term sustainable development of hospitals. This paper first identified the key indicators affecting the privacy disclosure of big data in health management, and then established the risk access control model based on the fuzzy theory, which was used for the management of big data in intelligent medical treatment, and solves the problem of inaccurate experimental results due to the lack of real data when dealing with actual problems. Finally, the model is compared with the results calculated by the fuzzy tool set in Matlab. The results verify that the model is effective in assessing the current safety risks and predicting the range of different risk factors, and the prediction accuracy can reach more than 90%. |
doi_str_mv | 10.1007/s10729-019-09490-4 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2263322475</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2263322475</sourcerecordid><originalsourceid>FETCH-LOGICAL-c465t-3eb58b29f60962c00a04319581786bb0072a875974d66549c49de0071f79e50a3</originalsourceid><addsrcrecordid>eNp9kM1rFjEQxhex2Fr9BzxIwIuXtZPvzbGU-gEFLy14EEI2O_u-W3c3muQV-t87dasFDx7ChMxvnjzzNM0rDu84gD0rHKxwLXA6Tjlo1ZPmhGsrWic795TusjOtMwKOm-el3AKABsOfNceSS2pJe9J8PWff8_QzxDuqqWKsU1rZgnWfBjamzPYY5rpnMWRk_bRjQ6iBLWENO1xwrawPBQdGM3kq31iIEUthMa01p_lFczSGueDLh3ra3Ly_vL742F59_vDp4vyqjcro2krsddcLNxogsxEggJLc6Y7bzvQ9rSpCZ7WzajBGKxeVG5Be-WgdagjytHm76dIKPw5Yql-mEnGew4rpULwQRkohlNWEvvkHvU2HvJK7e0qQutSSKLFRMadSMo6eQlpCvvMc_H32fsveU_b-d_Ze0dDrB-lDv-Dwd-RP2ASwDUDKZyqPmlYqDR2YL4TIDSnUXHeYH-395-dfEIKXXg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2262549353</pqid></control><display><type>article</type><title>A privacy protection method for health care big data management based on risk access control</title><source>MEDLINE</source><source>SpringerLink Journals</source><source>EBSCOhost Business Source Complete</source><creator>Shi, Mingyue ; Jiang, Rong ; Hu, Xiaohan ; Shang, Jingwei</creator><creatorcontrib>Shi, Mingyue ; Jiang, Rong ; Hu, Xiaohan ; Shang, Jingwei</creatorcontrib><description>With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the problem of patient privacy leakage is the most serious. Therefore, strengthening the information management of intelligent health care in the era of big data is an important part of the long-term sustainable development of hospitals. This paper first identified the key indicators affecting the privacy disclosure of big data in health management, and then established the risk access control model based on the fuzzy theory, which was used for the management of big data in intelligent medical treatment, and solves the problem of inaccurate experimental results due to the lack of real data when dealing with actual problems. Finally, the model is compared with the results calculated by the fuzzy tool set in Matlab. The results verify that the model is effective in assessing the current safety risks and predicting the range of different risk factors, and the prediction accuracy can reach more than 90%.</description><identifier>ISSN: 1386-9620</identifier><identifier>EISSN: 1572-9389</identifier><identifier>DOI: 10.1007/s10729-019-09490-4</identifier><identifier>PMID: 31338637</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Access control ; Big Data ; Business and Management ; Computer Security ; Confidentiality ; Data Anonymization ; Data Management - methods ; Data Management - standards ; Econometrics ; Fuzzy Logic ; Health Administration ; Health Care Sector ; Health Informatics ; Humans ; Management ; Operations Research/Decision Theory ; Privacy</subject><ispartof>Health care management science, 2020-09, Vol.23 (3), p.427-442</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2019.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-3eb58b29f60962c00a04319581786bb0072a875974d66549c49de0071f79e50a3</citedby><cites>FETCH-LOGICAL-c465t-3eb58b29f60962c00a04319581786bb0072a875974d66549c49de0071f79e50a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10729-019-09490-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10729-019-09490-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31338637$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shi, Mingyue</creatorcontrib><creatorcontrib>Jiang, Rong</creatorcontrib><creatorcontrib>Hu, Xiaohan</creatorcontrib><creatorcontrib>Shang, Jingwei</creatorcontrib><title>A privacy protection method for health care big data management based on risk access control</title><title>Health care management science</title><addtitle>Health Care Manag Sci</addtitle><addtitle>Health Care Manag Sci</addtitle><description>With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the problem of patient privacy leakage is the most serious. Therefore, strengthening the information management of intelligent health care in the era of big data is an important part of the long-term sustainable development of hospitals. This paper first identified the key indicators affecting the privacy disclosure of big data in health management, and then established the risk access control model based on the fuzzy theory, which was used for the management of big data in intelligent medical treatment, and solves the problem of inaccurate experimental results due to the lack of real data when dealing with actual problems. Finally, the model is compared with the results calculated by the fuzzy tool set in Matlab. The results verify that the model is effective in assessing the current safety risks and predicting the range of different risk factors, and the prediction accuracy can reach more than 90%.</description><subject>Access control</subject><subject>Big Data</subject><subject>Business and Management</subject><subject>Computer Security</subject><subject>Confidentiality</subject><subject>Data Anonymization</subject><subject>Data Management - methods</subject><subject>Data Management - standards</subject><subject>Econometrics</subject><subject>Fuzzy Logic</subject><subject>Health Administration</subject><subject>Health Care Sector</subject><subject>Health Informatics</subject><subject>Humans</subject><subject>Management</subject><subject>Operations Research/Decision Theory</subject><subject>Privacy</subject><issn>1386-9620</issn><issn>1572-9389</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kM1rFjEQxhex2Fr9BzxIwIuXtZPvzbGU-gEFLy14EEI2O_u-W3c3muQV-t87dasFDx7ChMxvnjzzNM0rDu84gD0rHKxwLXA6Tjlo1ZPmhGsrWic795TusjOtMwKOm-el3AKABsOfNceSS2pJe9J8PWff8_QzxDuqqWKsU1rZgnWfBjamzPYY5rpnMWRk_bRjQ6iBLWENO1xwrawPBQdGM3kq31iIEUthMa01p_lFczSGueDLh3ra3Ly_vL742F59_vDp4vyqjcro2krsddcLNxogsxEggJLc6Y7bzvQ9rSpCZ7WzajBGKxeVG5Be-WgdagjytHm76dIKPw5Yql-mEnGew4rpULwQRkohlNWEvvkHvU2HvJK7e0qQutSSKLFRMadSMo6eQlpCvvMc_H32fsveU_b-d_Ze0dDrB-lDv-Dwd-RP2ASwDUDKZyqPmlYqDR2YL4TIDSnUXHeYH-395-dfEIKXXg</recordid><startdate>20200901</startdate><enddate>20200901</enddate><creator>Shi, Mingyue</creator><creator>Jiang, Rong</creator><creator>Hu, Xiaohan</creator><creator>Shang, Jingwei</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>OQ6</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7X7</scope><scope>7XB</scope><scope>87Z</scope><scope>88C</scope><scope>88E</scope><scope>8AO</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>FYUFA</scope><scope>F~G</scope><scope>GHDGH</scope><scope>K60</scope><scope>K6~</scope><scope>K9.</scope><scope>L.-</scope><scope>M0C</scope><scope>M0S</scope><scope>M0T</scope><scope>M1P</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope></search><sort><creationdate>20200901</creationdate><title>A privacy protection method for health care big data management based on risk access control</title><author>Shi, Mingyue ; Jiang, Rong ; Hu, Xiaohan ; Shang, Jingwei</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c465t-3eb58b29f60962c00a04319581786bb0072a875974d66549c49de0071f79e50a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Access control</topic><topic>Big Data</topic><topic>Business and Management</topic><topic>Computer Security</topic><topic>Confidentiality</topic><topic>Data Anonymization</topic><topic>Data Management - methods</topic><topic>Data Management - standards</topic><topic>Econometrics</topic><topic>Fuzzy Logic</topic><topic>Health Administration</topic><topic>Health Care Sector</topic><topic>Health Informatics</topic><topic>Humans</topic><topic>Management</topic><topic>Operations Research/Decision Theory</topic><topic>Privacy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shi, Mingyue</creatorcontrib><creatorcontrib>Jiang, Rong</creatorcontrib><creatorcontrib>Hu, Xiaohan</creatorcontrib><creatorcontrib>Shang, Jingwei</creatorcontrib><collection>ECONIS</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>Health Research Premium Collection</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Healthcare Administration Database</collection><collection>Medical Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><jtitle>Health care management science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shi, Mingyue</au><au>Jiang, Rong</au><au>Hu, Xiaohan</au><au>Shang, Jingwei</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A privacy protection method for health care big data management based on risk access control</atitle><jtitle>Health care management science</jtitle><stitle>Health Care Manag Sci</stitle><addtitle>Health Care Manag Sci</addtitle><date>2020-09-01</date><risdate>2020</risdate><volume>23</volume><issue>3</issue><spage>427</spage><epage>442</epage><pages>427-442</pages><issn>1386-9620</issn><eissn>1572-9389</eissn><abstract>With the rapid development of modern information technology, the health care industry is entering a critical stage of intelligence. Faced with the growing health care big data, information security issues are becoming more and more prominent in the management of smart health care, especially the problem of patient privacy leakage is the most serious. Therefore, strengthening the information management of intelligent health care in the era of big data is an important part of the long-term sustainable development of hospitals. This paper first identified the key indicators affecting the privacy disclosure of big data in health management, and then established the risk access control model based on the fuzzy theory, which was used for the management of big data in intelligent medical treatment, and solves the problem of inaccurate experimental results due to the lack of real data when dealing with actual problems. Finally, the model is compared with the results calculated by the fuzzy tool set in Matlab. The results verify that the model is effective in assessing the current safety risks and predicting the range of different risk factors, and the prediction accuracy can reach more than 90%.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>31338637</pmid><doi>10.1007/s10729-019-09490-4</doi><tpages>16</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1386-9620 |
ispartof | Health care management science, 2020-09, Vol.23 (3), p.427-442 |
issn | 1386-9620 1572-9389 |
language | eng |
recordid | cdi_proquest_miscellaneous_2263322475 |
source | MEDLINE; SpringerLink Journals; EBSCOhost Business Source Complete |
subjects | Access control Big Data Business and Management Computer Security Confidentiality Data Anonymization Data Management - methods Data Management - standards Econometrics Fuzzy Logic Health Administration Health Care Sector Health Informatics Humans Management Operations Research/Decision Theory Privacy |
title | A privacy protection method for health care big data management based on risk access control |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T16%3A18%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=A%20privacy%20protection%20method%20for%20health%20care%20big%20data%20management%20based%20on%20risk%20access%20control&rft.jtitle=Health%20care%20management%20science&rft.au=Shi,%20Mingyue&rft.date=2020-09-01&rft.volume=23&rft.issue=3&rft.spage=427&rft.epage=442&rft.pages=427-442&rft.issn=1386-9620&rft.eissn=1572-9389&rft_id=info:doi/10.1007/s10729-019-09490-4&rft_dat=%3Cproquest_cross%3E2263322475%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2262549353&rft_id=info:pmid/31338637&rfr_iscdi=true |