A comprehensive survey on secure healthcare data processing with homomorphic encryption: attacks and defenses.
Healthcare data has risen as a top target for cyberattacks due to the rich amount of sensitive patient information. This negatively affects the potential of advanced analytics and collaborative research in healthcare. Homomorphic encryption (HE) has emerged as a promising technology for securing sen...
| Publicado en: | Discover Public Health Vol. 22; no. 1; pp. 1 - 30 |
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| Autores principales: | , , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Springer Nature
4/5/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184303017&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184303017 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 30050774 NM7Z jtl: Discover Public Health issn: 30050774 maglogo: N pubinfo: dt: 4/5/2025 vid: 22 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184303017 184303017 184303017 10.1186/s12982-025-00505-w 184303017 ppf: 1 ppct: 29 formats: tig: atl: A comprehensive survey on secure healthcare data processing with homomorphic encryption: attacks and defenses. aug: au: Lee, Chian Hui Lim, King Hann Eswaran, Sivaraman affil: https://ror.org/024fm2y42 Department of Electrical and Computer Engineering, Curtin University Malaysia, CDT 250, 98009, Miri, Sarawak, Malaysia sug: subj: Health Care Industry Data Analytics Data Security Encryption Methods Privacy and Confidentiality Data Breach Prevention and Control Electronic Health Records Machine Learning Public Health Power Analysis Algorithms ab: Healthcare data has risen as a top target for cyberattacks due to the rich amount of sensitive patient information. This negatively affects the potential of advanced analytics and collaborative research in healthcare. Homomorphic encryption (HE) has emerged as a promising technology for securing sensitive healthcare data while enabling computations on encrypted information. This paper conducts a background survey of HE and its various types. It discusses Partially Homomorphic Encryption (PHE), Somewhat Homomorphic Encryption (SHE), Fully Homomorphic Encryption (FHE) and Fully Leveled Homomorphic Encryption (FLHE). A critical analysis of these encryption paradigms' theoretical foundations, implementation schemes, and practical applications in healthcare contexts is presented. The survey encompasses diverse healthcare domains. It demonstrates HE's versatility in securing electronic health records (EHRs), enabling privacy-preserving genomic data analysis, protecting medical imaging, facilitating privacy-preserving machine learning (ML), supporting secure federated learning, ensuring confidentiality in clinical trials, and enhancing remote monitoring and telehealth services. A comprehensive examination of potential vulnerabilities in HE systems is conducted. The research systematically investigates various attack vectors, including side-channel attacks, key recovery attacks, chosen plaintext attacks (CPA), chosen ciphertext attacks (CCA), known plaintext attacks (KPA), fault injection attacks (FIA), and lattice attacks. A detailed analysis of potential defense mechanisms and mitigation strategies is provided for each identified threat. The analysis underscores the importance of HE for long-term security and sustainability in healthcare systems. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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