04025nam a22005655i 4500 978-3-031-76922-1 DE-He213 20251030114616.0 cr nn 008mamaa 241213s2024 sz | s |||| 0|eng d 9783031769221 978-3-031-76922-1 10.1007/978-3-031-76922-1 doi TK5101-5105.9 TJK bicssc TEC041000 bisacsh TJK thema Zhu, Dan. author. aut http://id.loc.gov/vocabulary/relators/aut 88869 Privacy-Preserving Techniques with e-Healthcare Applications [electronic resource] / by Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XII, 174 p. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Wireless Networks, 2366-1445 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. 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. Telecommunication. Medical informatics. 60693 Computational intelligence. 64052 Communications Engineering, Networks. Health Informatics. Computational Intelligence. 64052 Feng, Dengguo. author. aut http://id.loc.gov/vocabulary/relators/aut 88870 Shen, Xuemin (Sherman). author. aut http://id.loc.gov/vocabulary/relators/aut 70857 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031769214 Printed edition: 9783031769238 Printed edition: 9783031769245 Wireless Networks, 2366-1445 67497 https://doi.org/10.1007/978-3-031-76922-1 ZDB-2-SME ZDB-2-SXM 65683 65683