Zhu / Feng / Shen

Privacy-Preserving Techniques with e-Healthcare Applications

Springer

ISBN 978-3-031-76921-4

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Bibliografische Daten

Fachbuch

Buch. Hardcover

2024

Bibliographien.

In englischer Sprache

Umfang: xii, 174 S.

Format (B x L): 15,5 x 23,5 cm

Verlag: Springer

ISBN: 978-3-031-76921-4

Weiterführende bibliografische Daten

Das Werk ist Teil der Reihe: Wireless Networks

auch verfügbar als eBook (PDF) für 139,09 €

Produktbeschreibung

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.

Autorinnen und Autoren

Kundeninformationen

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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