Privacy-Preserving Techniques with e-Healthcare Applications

di Dan Zhu (Autore)

Dengguo Feng (Autore)

Xuemin (Sherman) Shen (Autore)

Springer International Publishing AG, 2024

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.

• Autore: Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen
• Editore: Springer International Publishing AG
• Collana: Wireless Networks
• Anno: 2024
• Rilegatura: Hardback
• Pagine: 174 p.
• Dimensioni: 235 x 155 mm
• EAN: 9783031769214

Testo in English