Privacy-Preserving Techniques with e-Healthcare Applications
Autor: | Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen |
---|---|
EAN: | 9783031769221 |
eBook Format: | |
Sprache: | Englisch |
Produktart: | eBook |
Veröffentlichungsdatum: | 13.12.2024 |
Kategorie: | |
Schlagworte: | Privacy Preservation;E-Healthcare Services;Metric Selection;Data Clustering;Tree Structure;Genomic Data;Similarity Computing |
139,09 €*
Versandkostenfrei
Die Verfügbarkeit wird nach ihrer Bestellung bei uns geprüft.
Bücher sind in der Regel innerhalb von 1-2 Werktagen abholbereit.
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.