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Record Nr. |
UNINA9910917788503321 |
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Autore |
Zhu Dan |
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Titolo |
Privacy-Preserving Techniques with e-Healthcare Applications / / by Dan Zhu, Dengguo Feng, Xuemin (Sherman) Shen |
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Pubbl/distr/stampa |
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Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024 |
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ISBN |
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Edizione |
[1st ed. 2024.] |
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Descrizione fisica |
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1 online resource (184 pages) |
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Collana |
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Wireless Networks, , 2366-1445 |
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Altri autori (Persone) |
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FengDengguo |
ShenXuemin (Sherman) |
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Disciplina |
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Soggetti |
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Telecommunication |
Medical informatics |
Computational intelligence |
Communications Engineering, Networks |
Health Informatics |
Computational Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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. |
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