Privacy-Preserving Techniques with e-Healthcare Applications
€ 145,90
Informação do Produto
| ISBN | 9783031769214 |
|---|---|
| Autores | Dan Zhu, Dengguo Feng, Xuemin Shen |
| Editora | Springer International Publishing AG |
| Formato | Capa dura |
| Número de páginas | 174 |
| Data de Publicação | 14/12/2024 |
| Preço (iva excl.) | € 137,64 |
| Imposto | € 8,26 |
| Preço (iva inc.) | € 145,90 |
| Disponibilidade | 1 unidade em armazém externo |
Descrição do Produto
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.