Medical Data Privacy Handbook (Paperback, Softcover reprint of the original 1st ed. 2015)

This handbook covers Electronic Medical Record (EMR) systems, which enable the storage, management, and sharing of massive amounts of demographic, diagnosis, medication, and genomic information. It presents privacy-preserving methods for medical data, ranging from laboratory test results to doctors' comments. The reuse of EMR data can greatly benefit medical science and practice, but must be performed in a privacy-preserving way according to data sharing policies and regulations. Written by world-renowned leaders in this field, each chapter offers a survey of a research direction or a solution to problems in established and emerging research areas. The authors explore scenarios and techniques for facilitating the anonymization of different types of medical data, as well as various data mining tasks. Other chapters present methods for emerging data privacy applications and medical text de-identification, including detailed surveys of deployed systems. A part of the book is devoted to legislative and policy issues, reporting on the US and EU privacy legislation and the cost of privacy breaches in the healthcare domain. This reference is intended for professionals, researchers and advanced-level students interested in safeguarding medical data.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Release date:	March 2019
First published:	2015
Editors:	Aris Gkoulalas-Divanis • Grigorios Loukides
Dimensions:	235 x 155mm (L x W)
Format:	Paperback
Pages:	832
Edition:	Softcover reprint of the original 1st ed. 2015
ISBN-13:	978-3-319-79514-0
Categories:	Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Computer communications & networking > Network security Books > Medicine > General issues > Medical equipment & techniques > General Books > Computing & IT > Applications of computing > Databases > General
LSN:	3-319-79514-7
Barcode:	9783319795140

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