東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Privacy-preserving techniques with e...

Zhu, Dan.

Linked to FindBook

Google Book

Amazon

博客來

Privacy-preserving techniques with e-Healthcare applications

Record Type:	Electronic resources : Monograph/item
Title/Author:	Privacy-preserving techniques with e-Healthcare applications/ by 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.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Medical informatics - Security measures. -
Online resource:	https://doi.org/10.1007/978-3-031-76922-1
ISBN:	9783031769221

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

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

2074128
Medical informatics
--Security measures.

LC Class. No.: R859.7.S43

Dewey Class. No.: 610.28558

Privacy-preserving techniques with e-Healthcare applications
LDR:02956nmm a2200337 a 4500 001 2389210
003 DE-He213
005 20241213115313.0
006 m d
007 cr nn 008maaau
008 250916s2024 sz s 0 eng d
020 $a 9783031769221 $q (electronic bk.)
020 $a 9783031769214 $q (paper)
024 7 $a 10.1007/978-3-031-76922-1 $2 doi
035 $a 978-3-031-76922-1
040 $a GP $c GP
041 0 $a eng
050 4 $a R859.7.S43
072 7 $a TJK $2 bicssc
072 7 $a TEC041000 $2 bisacsh
072 7 $a TJK $2 thema
082 0 4 $a 610.28558 $2 23
090 $a R859.7.S43 $b Z63 2024
100 1 $a Zhu, Dan. $3 1297938
245 1 0 $a Privacy-preserving techniques with e-Healthcare applications $h [electronic resource] / $c by Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xii, 174 p. : $b ill., digital ; $c 24 cm.
490 1 $a Wireless networks, $x 2366-1445
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.
650 0 $a Medical informatics $x Security measures. $3 2074128
650 0 $a Data privacy. $3 3593153
650 1 4 $a Communications Engineering, Networks. $3 891094
650 2 4 $a Health Informatics. $3 892928
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Feng, Dengguo. $3 893758
700 1 $a Shen, X. $q (Xuemin) $3 2162431
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Wireless networks. $3 2162432
856 4 0 $u https://doi.org/10.1007/978-3-031-76922-1
950 $a Medicine (SpringerNature-11650)