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Privacy-preserving techniques with e-Healthcare applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Privacy-preserving techniques with e-Healthcare applicationsby Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen.
Author:	Zhu, Dan.
other author:	Feng, Dengguo.
Published:	Cham :Springer Nature Switzerland :2024.
Description:	xii, 174 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Medical informaticsSecurity measures.
Online resource:	https://doi.org/10.1007/978-3-031-76922-1
ISBN:	9783031769221$q(electronic bk.)

Privacy-preserving techniques with e-Healthcare applications
Zhu, Dan.

Privacy-preserving techniques with e-Healthcare applications[electronic resource] /by Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen. - Cham :Springer Nature Switzerland :2024. - xii, 174 p. :ill., digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..

Introduction -- An Overview of e-Healthcare -- Privacy-Preserving and Machine-Learning Techniques -- Privacy-Preserving Similar Patient Query Services over Genomic Data -- Privacy-Preserving Similarity Retrieval Services over Medical Images -- Privacy-Preserving Pre-diagnosis Services over Single-label Medical Records -- Privacy-Preserving Pre-diagnosis Services over Multi-label Medical Records -- Future Works -- Conclusion.

This book investigates novel accurate and efficient privacy-preserving techniques and their applications in e-Healthcare services. The authors first provide an overview and a general architecture of e-Healthcare and delve into discussions on various applications within the e-Healthcare domain. Simultaneously, they analyze the privacy challenges in e-Healthcare services. Then, in Chapter 2, the authors give a comprehensive review of privacy-preserving and machine learning techniques applied in their proposed solutions. Specifically, Chapter 3 presents an efficient and privacy-preserving similar patient query scheme over high-dimensional and non-aligned genomic data; Chapter 4 and Chapter 5 respectively propose an accurate and privacy-preserving similar image retrieval scheme and medical pre-diagnosis scheme over dimension-related medical images and single-label medical records; Chapter 6 presents an efficient and privacy-preserving multi-disease simultaneous diagnosis scheme over medical records with multiple labels. Finally, the authors conclude the monograph and discuss future research directions of privacy-preserving e-Healthcare services in Chapter 7. Studies the issues and challenges of privacy-preserving techniques applied in e-Healthcare services; Focuses on common and distinctive medical data, investigating accurate e-Healthcare services with privacy preservation; Proposes solutions with proof-of-concept prototypes, tested on real and simulated datasets.

ISBN: 9783031769221$q(electronic bk.)

Standard No.: 10.1007/978-3-031-76922-1doiSubjects--Topical Terms:

695572
Medical informatics
--Security measures.

LC Class. No.: R859.7.S43

Dewey Class. No.: 610.28558

Privacy-preserving techniques with e-Healthcare applications
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505 0 $a Introduction -- An Overview of e-Healthcare -- Privacy-Preserving and Machine-Learning Techniques -- Privacy-Preserving Similar Patient Query Services over Genomic Data -- Privacy-Preserving Similarity Retrieval Services over Medical Images -- Privacy-Preserving Pre-diagnosis Services over Single-label Medical Records -- Privacy-Preserving Pre-diagnosis Services over Multi-label Medical Records -- Future Works -- Conclusion.
520 $a This book investigates novel accurate and efficient privacy-preserving techniques and their applications in e-Healthcare services. The authors first provide an overview and a general architecture of e-Healthcare and delve into discussions on various applications within the e-Healthcare domain. Simultaneously, they analyze the privacy challenges in e-Healthcare services. Then, in Chapter 2, the authors give a comprehensive review of privacy-preserving and machine learning techniques applied in their proposed solutions. Specifically, Chapter 3 presents an efficient and privacy-preserving similar patient query scheme over high-dimensional and non-aligned genomic data; Chapter 4 and Chapter 5 respectively propose an accurate and privacy-preserving similar image retrieval scheme and medical pre-diagnosis scheme over dimension-related medical images and single-label medical records; Chapter 6 presents an efficient and privacy-preserving multi-disease simultaneous diagnosis scheme over medical records with multiple labels. Finally, the authors conclude the monograph and discuss future research directions of privacy-preserving e-Healthcare services in Chapter 7. Studies the issues and challenges of privacy-preserving techniques applied in e-Healthcare services; Focuses on common and distinctive medical data, investigating accurate e-Healthcare services with privacy preservation; Proposes solutions with proof-of-concept prototypes, tested on real and simulated datasets.
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