Privacy-Preserving Techniques with e-Healthcare Applications / by Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen.

Format:

Book

Author/Creator:

Zhu, Dan.

Contributor:

Feng, Dengguo.

Shen, Xuemin (Sherman).

Series:

Wireless Networks, 2366-1445

Language:

English

Subjects (All):

Telecommunication.

Medical informatics.

Computational intelligence.

Communications Engineering, Networks.

Health Informatics.

Computational Intelligence.

Local Subjects:

Communications Engineering, Networks.

Health Informatics.

Computational Intelligence.

Physical Description:

1 online resource (184 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2024.

Summary:

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.

Contents:

Introduction

An Overview of e-Healthcare

Privacy-Preserving and Machine-Learning Techniques

Privacy-Preserving Similar Patient Query Services over Genomic Data

Privacy-Preserving Similarity Retrieval Services over Medical Images

Privacy-Preserving Pre-diagnosis Services over Single-label Medical Records

Privacy-Preserving Pre-diagnosis Services over Multi-label Medical Records

Future Works

Conclusion.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9783031769221

3031769228

OCLC:

1482824592