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Anglický jazyk
Privacy-Preserving Techniques with e-Healthcare Applications
Autor: Dan Zhu
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... Viac o knihe
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O knihe
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.
- Vydavateľstvo: Springer
- Rok vydania: 2024
- Formát: Hardback
- Rozmer: 241 x 160 mm
- Jazyk: Anglický jazyk
- ISBN: 9783031769214
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