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2024 | OriginalPaper | Chapter

Privacy-Preserving Retrieval Scheme Over Encrypted Medical Records with Relevance Ranking

Authors : Wanting Lei, Xiehua Li, Yingzhu Wang, Xiaoyu Mei

Published in: Parallel and Distributed Computing, Applications and Technologies

Publisher: Springer Nature Singapore

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Abstract

Electronic medical records (EMRs) contain a large amount of highly private and sensitive information of patients and medical institutions. For privacy concerns, EMRs are usually encrypted before outsourcing them to the cloud storage platform. However, it is difficult to retrieve the encrypted EMRs accurately and efficiently. The existing encrypted data retrieval schemes can hardly achieve the goals of fuzzy multi-keyword search, relevance ranking and high retrieval accuracy. Thus, this paper proposes a Privacy-preserving Retrieval scheme over Encrypted Medical Records (PREMR) that can satisfy those goals. We utilize the Possibility-Levenshtein based Spelling Corrector (PLSC) to support fuzzy multiple input keywords. A homomorphic-based encryption algorithm is proposed for relevance score encryption and calculation so that the encrypted medical records can be ranked without leaking private information. We theoretically prove that our scheme can achieve data confidential and privacy preserving. With the experiments’ evaluation, we analyze the costs and efficiency of our scheme. Finally, the comparison of PREMR with other related schemes shows that our scheme is more efficient and secure.

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Metadata
Title
Privacy-Preserving Retrieval Scheme Over Encrypted Medical Records with Relevance Ranking
Authors
Wanting Lei
Xiehua Li
Yingzhu Wang
Xiaoyu Mei
Copyright Year
2024
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-8211-0_9

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