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6 June 2024 EPTDMS: efficient and privacy-preserving top-k disease matching scheme for cloud-assisted e-healthcare system
Ou Ruan, Xin Jiang
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Proceedings Volume 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024); 1317504 (2024) https://doi.org/10.1117/12.3031898
Event: 4th International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 2024, Sanya, China
Abstract
In modern e-healthcare systems, healthcare providers usually store users' data in cloud servers. Users wish to obtain relevant diagnostic files through data generated by body sensors. We propose an efficient and privacy-preserving Top- k disease matching scheme (called EPTDMS). EPTDMS uses Density-Sensitive Hashing (DSH) to implement fuzzy search in stage one, employs the cosine value to sort the relevant result, and obtains patient diagnostic files. Improvements are made to address the problems of low matching efficiency, high computational overhead, and high communication volume of most privacy-preserving matching schemes. This scheme achieves disease matching with low computation and communication overhead and reduces the average query time.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Ou Ruan and Xin Jiang "EPTDMS: efficient and privacy-preserving top-k disease matching scheme for cloud-assisted e-healthcare system", Proc. SPIE 13175, International Conference on Computer Network Security and Software Engineering (CNSSE 2024), 1317504 (6 June 2024); https://doi.org/10.1117/12.3031898
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KEYWORDS
Diagnostics
Clouds
Diseases and disorders
Computer security
Fuzzy logic
Medicine
Symmetric key encryption
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