LEADER 03499nam 2200565 a 450 001 9910437908803321 005 20200520144314.0 010 $a1-4614-5668-1 024 7 $a10.1007/978-1-4614-5668-1 035 $a(CKB)2670000000277830 035 $a(EBL)1081973 035 $a(OCoLC)820329418 035 $a(SSID)ssj0000799364 035 $a(PQKBManifestationID)11462989 035 $a(PQKBTitleCode)TC0000799364 035 $a(PQKBWorkID)10755218 035 $a(PQKB)11083288 035 $a(DE-He213)978-1-4614-5668-1 035 $a(MiAaPQ)EBC1081973 035 $a(PPN)168303639 035 $a(EXLCZ)992670000000277830 100 $a20120827d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAnonymization of electronic medical records to support clinical analysis /$fAris Gkoulalas-Divanis 205 $a1st ed. 2013. 210 $aNew York $cSpringer$d2013 215 $a1 online resource (86 p.) 225 0$aSpringerBriefs in electrical and computer engineering,$x2191-8112 300 $aDescription based upon print version of record. 311 $a1-4614-5667-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction -- Overview of patient data anonymization -- Re-identification of clinical data through diagnosis information -- Preventing re-identification while supporting GWAS -- Case study on electronic medical records data -- Conclusions and open research challenges -- Index. 330 $aAnonymization of Electronic Medical Records to Support Clinical Analysis closely examines the privacy threats that may arise from medical data sharing, and surveys the state-of-the-art methods developed to safeguard data against these threats. To motivate the need for computational methods, the book first explores the main challenges facing the privacy-protection of medical data using the existing policies, practices and regulations. Then, it takes an in-depth look at the popular computational privacy-preserving methods that have been developed for demographic, clinical and genomic data sharing, and closely analyzes the privacy principles behind these methods, as well as the optimization and algorithmic strategies that they employ. Finally, through a series of in-depth case studies that highlight data from the US Census as well as the Vanderbilt University Medical Center, the book outlines a new, innovative class of privacy-preserving methods designed to ensure the integrity of transferred medical data for subsequent analysis, such as discovering or validating associations between clinical and genomic information. Anonymization of Electronic Medical Records to Support Clinical Analysis is intended for professionals as a reference guide for safeguarding the privacy and data integrity of sensitive medical records. Academics and other research scientists will also find the book invaluable. 410 0$aSpringerBriefs in Electrical and Computer Engineering,$x2191-8112 606 $aMedical records$xData processing 615 0$aMedical records$xData processing. 676 $a651.5042610285 700 $aGkoulalas-Divanis$b Aris$01058862 701 $aLoukides$b Grigorios$01755177 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910437908803321 996 $aAnonymization of electronic medical records to support clinical analysis$94191864 997 $aUNINA