Title: WeightedSim: privacy-preserving weighted similarity query over encrypted healthcare data

Authors: Guojun Tang; Rongxing Lu; Mohammad Mamun

Addresses: Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada ' Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada ' National Research Council Canada (NRC-CNRC), Government of Canada, Fredericton, NB, Canada

Abstract: The development of smart applications in e-healthcare has aroused the exponential growth of healthcare data. Therefore, the data owner tends to outsource them to powerful cloud servers, which can provide query services. However, for privacy concerns, the data owner may outsource the encrypted data instead of plaintexts. Moreover, when using the data query service, query users may search the data based on their preferences. To address the aforementioned issues, in this paper, we propose WeightedSim, an efficient and privacy-preserving weighted similarity range query scheme for outsourced healthcare data. Specifically, we first develop an encrypted R-tree index by utilising the symmetric homomorphic encryption (SHE) technique and then employ it to perform a weighted similarity range query under the two cloud servers model. We analyse the security of our scheme to be selectively secure when the SHE is semantically secure against CPA and also conduct extensive experiments to validate the scheme's efficacy.

Keywords: privacy-preserving data query; R-tree; symmetric homomorphic encryption; SHE.

DOI: 10.1504/IJACT.2024.138459

International Journal of Applied Cryptography, 2024 Vol.4 No.3/4, pp.143 - 155

Received: 14 Jan 2023
Accepted: 14 May 2023
Published online: 03 May 2024 *

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